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Data Warehousing & DataMinig 10IS74


Subject Code: 10IS74 I.A. Marks : 25
Hours/Week : 04 Exam Hours: 03
Total Hours : 52 Exam Marks:100

UNIT 1 6 Hours

Data Warehousing:
Introduction, Operational Data Stores (ODS), Extraction Transformation Loading (ETL), Data
Warehouses. Design Issues, Guidelines for Data Warehouse Implementation, Data Warehouse Metadata.

UNIT 2 6 Hours
Online Analytical Processing (OLAP): Introduction, Characteristics of OLAP systems,
Multidimensional view and Data cube, Data Cube Implementations, Data Cube operations,
Implementation of OLAP and overview on OLAP Softwares.
UNIT 3 6 Hours
Data Mining: Introduction, Challenges, Data Mining Tasks, Types of Data,Data Preprocessing,
Measures of Similarity and Dissimilarity, Data Mining Applications
UNIT 4 8 Hours
Association Analysis: Basic Concepts and Algorithms: Frequent Itemset Generation, Rule Generation,
Compact Representation of Frequent Itemsets, Alternative methods for generating Frequent Itemsets, FP
Growth Algorithm,Evaluation of Association Patterns

UNIT 5 6 Hours
Classification -1 : Basics, General approach to solve classification problem, Decision Trees, Rule Based
Classifiers, Nearest Neighbor Classifiers.

UNIT 6 6 Hours
Classification - 2 : Bayesian Classifiers, Estimating Predictive accuracy of classification methods,
Improving accuracy of clarification methods, Evaluation criteria for classification methods, Multiclass

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UNIT 7 8 Hours
Clustering Techniques: Overview, Features of cluster analysis, Types of Data and Computing Distance,
Types of Cluster Analysis Methods, Partitional Methods, Hierarchical Methods, Density Based Methods,
Quality and Validity of Cluster Analysis.

UNIT 8 6 Hours
Web Mining: Introduction, Web content mining, Text Mining, Unstructured Text, Text clustering,
Mining Spatial and Temporal Databases.

Text Books:
1. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson Education,
2. G. K. Gupta: Introduction to Data Mining with Case Studies, 3rdEdition, PHI, New Delhi, 2009.
Reference Books:
1. Arun K Pujari: Data Mining Techniques, 2nd Edition, UniversitiesPress, 2009.
2. Jiawei Han and Micheline Kamber: Data Mining - Concepts and Techniques, 2nd Edition, Morgan
Kaufmann Publisher, 2006.
3. Alex Berson and Stephen J. Smith: Data Warehousing,

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Unit-1 : Data Warehousing: Page No.

1.1 Introduction, 5
1.2 Operational Data Stores (ODS) 6
1.3 Extraction Transformation Loading (ETL) 8
1.4 Data Warehouses. 12
1.5 Design Issues, 17
1.6 Guidelines for Data Warehouse Implementation, 24
1.7 Data Warehouse Metadata. 27

UNIT2: Online Analytical Processing OLAP

2.1 Introduction, 30
2.2 Characteristics of OLAP systems, 34
2.3 Multidimensional view and Data cube, 38
2.4 Data Cube Implementations, 45
2.5 Data Cube operations, 50
2.6 Implementation of OLAP 56
2.7 Overview on OLAP Softwares. 57

UNIT 3: Data Mining

3.1 Introduction, 60
3.2Challenges, 61
3.3Data Mining Tasks, 67
3.4 Types of Data, 73
3.5 Data Preprocessing, 69
3.6 Measures of Similarity and Dissimilarity, Data Mining Applications 84
UNIT 4: Association Analysis:
4.1 Basic Concepts and Algorithms 87
4.2 Frequent Itemset Generation, 91

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4.3Rule Generation, 97
4.4 Compact Representation of Frequent Itemsets, 99
4.5 Alternative methods for generating Frequent Itemsets, 103
4.6 FP Growth Algorithm,Evaluation of Association Patterns 103


5.1Classification -1: Basics, 107
5.2 General approach to solve classification problem, 107
5.3 Decision Trees, 110
5.4 Rule Based Classifiers, 124
5.5 Nearest Neighbor Classifiers. 129
5.6 Classification - 2: Bayesian Classifiers, 131
UNIT 7 Clustering Techniques:
7.1Overview, 132
7.2 Features of cluster analysis, 132
7.3 Types of Data and Computing Distance, 133
7.4 Types of Cluster Analysis Methods, Partitional Methods, Hierarchical Methods, Density
Based Methods, 133
7.5 Quality and Validity of Cluster Analysis. 134

UNIT 8 Web Mining:

8.1Introduction, 135
8.2 Web content mining, 135
8.3 Text Mining, 136
8.4Unstructured Text, 136
8.5 Text clustering, 137
8.6 Mining Spatial and Temporal Databases. 138

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Major enterprises have many computers that run a variety of enterprise applications.
For an enterprise with branches in many locations, the branches may have their own
systems. For example, in a university with only one campus, the library may run its own
catalog and borrowing database system while the student administration may have own
systems running on another machine. There might be a separate finance system, a
property and facilities management system and another for human resources
management. A large company might have the following system.
Human Resources
Sales leads
Web sales
Customer support
Such systems are called online transaction processing (OLTP) systems. The OLTP
systems are mostly relational database systems designed for transaction processing. The
performance of OLTP systems is usually very important since such systems are used to
support the users (i.e. staff) that provide service to the customers. The systems

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therefore must be able to deal with insert and update operations as well as answering
simple queries quickly.

An ODS has been defined by Inmon and Imhoff (1996) as a subject-oriented,

integrated, volatile, current valued data store, containing only corporate detailed data. A
data warehouse is a reporting database that contains relatively recent as well as historical
data and may also contain aggregate data.
The ODS is subject-oriented. That is, it is organized around the major data
subjects of an enterprise. In a university, the subjects might be students, lecturers and
courses while in company the subjects might be customers, salespersons and products.
The ODS is integrated. That is, it is a collection of subject-oriented data from a
variety of systems to provide an enterprise-wide view of the data.
The ODS is current valued. That is, an ODS is up-to-date and reflects the current
status of the information. An ODS does not include historical data. Since the OLTP
systems data is changing all the time, data from underlying sources refresh the ODS as
regularly and frequently as possible.
The ODS is volatile. That is, the data in the ODS changes frequently as new
information refreshes the ODS.
The ODS is detailed. That is, the ODS is detailed enough to serve the needs of the
operational management staff in the enterprise. The granularity of the data in the ODS
does not have to be exactly the same as in the source OLTP system.

ODS Design and Implementation

The extraction of information from source databases needs to be efficient and the quality
of data needs to be maintained. Since the data is refreshed regularl and frequently,
suitable checks are required to ensure quality of data after each refresh. An ODS would
of course be required to satisfy normal integrity constraints, for example, existential
integrity, referential integrity and appropriate action to deal with nulls. An ODS is a read
only database other than regular refreshing by the OLTP systems. Users should not be
allowed to update ODS information.

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Populating an ODS involves an acquisition process of extracting, transforming

and loading data from OLTP source systems. This process is ETL. Completing
populating the database, checking for anomalies and testing for performance are
necessary before an ODS system can go online.

Source Systems ETL ODS

End Users

Loading reports

Data Source
Oracle Wefsba-fbdased

IMS Initial
loading+ Other
refreshing Applications


Flat Files Data Data

Warehouse Marts

Fig :1.1 A possible Operational Data Store structure

Zero Latency Enterprise (ZLE)

The Gantner Group has used a term Zero Latency Enterprise (ZLE) for near real-time
integration of operational data so that there is no significant delay in getting information
from one part or one system of an enterprise to another system that needs the information.
The heart of a ZLE system is an operational data store.
A ZLE data store is something like an ODS that is integrated and up-to-date. The
aim of a ZLE data store is to allow management a single view of enterprise information

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by bringing together relevant data in real-time and providing management a 360-degree

view of the customer.
A ZLE usually has the following characteristics. It has a unified view of the
enterprise operational data. It has a high level of availability and it involves online
refreshing of information. The achieve these, a ZLE requires information that is as
current as possible. Since a ZLE needs to support a large number of concurrent users, for
example call centre users, a fast turnaround time for transactions and 24/7 availability is

1.3 ETL

An ODS or a data warehouse is based on a single global schema that integrates and
consolidates enterprise information from many sources. Building such a system requires
data acquisition from OLTP and legacy systems. The ETL process involves extracting,
transforming and loading data from source systems. The process may sound very simple
since it only involves reading information from source databases, transforming it to fit the
ODS database model and loading it in the ODS.
As different data sources tend to have different conventions for coding
information and different standards for the quality of information, building an ODS
requires data filtering, data cleaning, and integration.
The following examples show the importance of data cleaning:
If an enterprise wishes to contact its customers or its suppliers, it is essential that a
complete, accurate and up-to-date list of contact addresses, email addresses and
telephone numbers be available. Correspondence sent to a wrong address that is
then redirected does not create a very good impression about the enterprise.
If a customer or supplier calls, the staff responding should be quickly ale to find
the person in the enterprise database but this requires that the callers name or
his/her company name is accurately listed in the database.
If a customer appears in the databases with two or more slightly different names
or different account numbers, it becomes difficult to update the customers

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ETL requires skills in management, business analysis and technology and is often a
significant component of developing an ODS or a data warehouse. The ETL process
tends to be different for every ODS and data warehouse since every system is different. It
should not be assumed that an off-the-shelf ETL system can magically solve all ETL
ETL Functions

The ETL process consists of data extraction from source systems, data transformation
which includes data cleaning, and loading data in the ODS or the data warehouse.
Transforming data that has been put in a staging area is a rather complex phase of
ETL since a variety of transformations may be required. Large amounts of data from
different sources are unlikely to match even if belonging to the same person since
people using different conventions and different technology and different systems
would have
created records at different times in a different environment for different purposes.
Building an integrated database from a number of such source systems may involve
solving some or all of the following problems, some of which may be single-source
problems while others may be multiple-source problems:

1. Instance identity problem: The same customer or client may be represented

slightly different in different source systems. For example, my name is
represented as Gopal Gupta in some systems and as GK Gupta in others. Given
that the name is unusual for data entry staff in Western countries, it is sometimes
misspelled as Gopal Gopta or Gopal Gupta or some other way. The name may
also be represented as Professor GK Gupta, Dr GK Gupta or Mr GK Gupta.
There is thus a possibility of mismatching between the different systems that
needs to be identified and corrected.

2. Data errors: Many different types of data errors other than identity errors are
possible. For example:
Data may have some missing attribute values.

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Coding of some values in one database may not match with coding in
other databases (i.e. different codes with the same meaning or same code
for different meanings)
Meanings of some code values may not be known.
There may be duplicate records.
There may be wrong aggregations.
There may be inconsistent use of nulls, spaces and empty values.
Some attribute values may be inconsistent (i.e. outside their domain)
Some data may be wrong because of input errors.
There may be inappropriate use of address lines.
There may be non-unique identifiers.
The ETL process needs to ensure that all these types of errors and others are
resolved using a sound Technology.

3. Record linkage problem: Record linkage relates to the problem of linking

information from different databases that relate to the same customer or client.
The problem can arise if a unique identifier is not available in all databases that
are being linked. Perhaps records from a database are being linked to records
from a legacy system or to information from a spreadsheet. Record linkage can
involve a large number of record comparisons to ensure linkages that have a high
level of accuracy.

4. Semantic integration problem: This deals with the integration of information

found in heterogeneous OLTP and legacy sources. Some of the sources may be
relational, some may not be. Some may be even in text documents. Some data
may be character strings while others may be integers.

5. Data integrity problem: This deals with issues like referential integrity, null
values, domain of values, etc.

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Overcoming all these problems is often a very tedious work. Many errors can be difficult
to identify. In some cases one may be forced to ask the question how accurate the data
ought to be since improving the accuracy is always going to require more and more
resources and completely fixing all problems may be unrealistic.
Checking for duplicates is not always easy. The data can be sorted and duplicates
removed although for large files this can be expensive. In some cases the duplicate
records are not identical. In these cases checks for primary key may be required. If more
than one record has the same primary key then it is likely to be because of duplicates.

A sound theoretical background is being developed for data cleaning techniques. It

has been suggested that data cleaning should be based on the following five steps:

1. Parsing: Parsing identifies various components of the source data files and then
establishes relationships between those and the fields in the target files. The
classical example of parsing is identifying the various components of a persons
name and address.
2. Correcting: Correcting the identified components is usually based on a variety
of sophisticated techniques including mathematical algorithms. Correcting may
involve use of other related information that may be available in the enterprise.
3. Standardizing: Business rules of the enterprise may now be used to transform
the data to standard form. For example, in some companies there might be rules
on how name and address are to be represented.
4. Matching: Much of the data extracted from a number of source systems is likely
to be related. Such data needs to be matched.
5. Consolidating: All corrected, standardized and matched data can now be
consolidated to build a single version of the enterprise data.

Selecting an ETL Tool

Selection of an appropriate ETL Tool is an important decision that has to be made in

choosing components of an ODS or data warehousing application. The ETL tool is
required to provide coordinated access to multiple data sources so that relevant data may

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be extracted from them. An ETL tool would normally include tools for data cleansing,
reorganization, transformation, aggregation, calculation and automatic loading of data
into the target database.
An ETL tool should provide an easy user interface that allows data cleansing and
data transformation rules to be specified using a point-and-click approach. When all
mappings and transformations have been specified, the ETL tool should automatically
generate the data extract/transformation/load programs, which typically run in batch


Data warehousing is a process for assembling and managing data from various sources
for the purpose of gaining a single detailed view of an enterprise. Although there are
several definitions of data warehouse, a widely accepted definition by Inmon (1992) is an
integrated subject-oriented and time-variant repository of information in support of
managements decision making process. The definition of an ODS to except that an ODS
is a current-valued data store while a data warehouse is a time-variant repository of data.
The benefits of implementing a data warehouse are as follows:
To provide a single version of truth about enterprise information. This may appear
rather obvious but it is not uncommon in an enterprise for two database systems to
have two different versions of the truth. In many years of working in universities,
I have rarely found a university in which everyone agrees with financial figures of
income and expenditure at each reporting time during the year.
To speed up ad hoc reports and queries that involve aggregations across many
attributes (that is, may GROUP BYs) which are resource intensive. The
managers require trends, sums and aggregations that allow, for example,
comparing this years performance to last years or preparation of forecasts for
next year.
To provide a system in which managers who do not have a strong technical
background are able to run complex queries. If the managers are able to access the

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information they require, it is likely to reduce the bureaucracy around the

To provide a database that stores relatively clean data. By using a good ETL
process, the data warehouse should have data of high quality. When errors are
discovered it may be desirable to correct them directly in the data warehouse and
then propagate the corrections to the OLTP systems.
To provide a database that stores historical data that may have been deleted from
the OLTP systems. To improve response time, historical data is usually not
retained in OLTP systems other than that which is required to respond to
customer queries. The data warehouse can then store the data that is purged from
the OLTP systems.

A useful way of showing the relationship between OLTP systems, a data warehouse and
an ODS is given in Figure 7.2. The data warehouse is more like long term memory of an
enterprise. The objectives in building the two systems, ODS and data warehouse, are
somewhat conflicting and therefore the two databases are likely to have different


Data warehouse
OLTP system

Figure 7.2 Relationship between OLTP, ODS and DW systems.

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In building and ODS, data warehousing is a process of integrating enterprise-wide data,

originating from a variety of sources, into a single repository. As shown in Figure 7.3, the
data warehouse may be a central enterprise-wide data warehouse for use by all the
decision makers in the enterprise or it may consist of a number of smaller data warehouse
(often called data marts or local data warehouses)
A data mart stores information for a limited number of subject areas. For
example, a company might have a data mart about marketing that supports marketing and
sales. The data mart approach is attractive since beginning with a single data mart is
relatively inexpensive and easier to implement.
A data mart may be used as a proof of data warehouse concept. Data marts can
also create difficulties by setting up silos of information although one may build
dependent data marts, which are populated form the central data warehouse.
Data marts are often the common approach for building a data warehouse since
the cost curve for data marts tends to be more linear. A centralized data warehouse
project can be very resource intensive and requires significant investment at the
beginning although overall costs over a number of years for a centralized data warehouse
and for decentralized data marts are likely to be similar.
A centralized warehouse can provide better quality data and minimize data
inconsistencies since the data quality is controlled centrally. The tools and procedures for
putting data in the warehouse can then be better controlled. Controlling data quality with
a decentralized approach is obviously more difficult. As with any centralized function,
though, the units or branches of an enterprise may feel no ownership of the centralized
warehouse may in some cases not fully cooperate with the administration of the
warehouse. Also, maintaining a centralized warehouse would require considerable
coordination among the various units if the enterprise is large and this coordination may
incur significant costs for the enterprise.
As an example of a data warehouse application we consider the
telecommunications industry which in most countries has become very competitive
during the last few years. If a company is able to identify a market trend before its
competitors do, then that can lead to a competitive advantage. What is therefore needed is
to analyse customer needs and behaviour in an attempt to better understand what the

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customers want and need. Such understanding might make it easier for a company
identify, develop, and deliver some relevant new products or new pricing schemes
retain and attract customers. It can also help in improving profitability since it can help
the company understand what type of customers are the most profitable.

Data Mart Data Mart Data Mart

Central Data Warehouse

Database Database Legacy

Figure 7.3 Simple structure of a data warehouse system.

ODS and DW Architecture

A typical ODS structure was shown in Figure 7.1. It involved extracting information
from source systems by using ETL processes and then storing the information in the
CICS ,Flat Files, Oracle

The ODS could then be used for producing a variety of reports for management.


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Data BI Tool
process process Data BI Tool
Extract process Mart
and Load
BI Tool

Daily Daily
Change (Staging Change
Process Area) Process Data
Operational Warehouse
Data Store (DW)

Figure 7.4 Another structure for ODS and DW

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The architecture of a system that includes an ODS and a data warehouse shown in Figure
7.4 is more complex. It involves extracting information from source systems by using an
ETL process and then storing the information in a staging database. The daily changes
also come to the staging area. Another ETL process is used to transform information
from the staging area to populate the ODS. The ODS is then used for supplying
information via another ETL process to the area warehouse which in turn feeds a number
of data marts that generate the reports required by management. It should be noted that
not all ETL processes in this architecture involve data cleaning, some may only involve
data extraction and transformation to suit the target systems.


There are a number of ways of conceptualizing a data warehouse. One approach is to

view it as a three-level structure. The lowest level consists of the OLTP and legacy
systems, the middle level consists of the global or central data warehouse while the top
level consists of local data warehouses or data marts. Another approach is possible if the
enterprise has an ODS. The three levels then might consist of OLTP and legacy systems
at the bottom, the ODS in the middle and the data warehouse at the top.
Whatever the architecture, a data warehouse needs to have a data model that can
form the basis for implementing it. To develop a data model we view a data warehouse as
a multidimensional structure consisting of dimensions, since that is an intuitive model
that matches the types of OLAP queries posed by management. A dimension is an
ordinate within a multidimensional structure consisting of a list of ordered values
(sometimes called members) just like the x-axis and y-axis values on a two-dimensional

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Number of
Degree Country


Figure 7.5 A simple example of a star schema.

A data warehouse model often consists of a central fact table and a set of
surrounding dimension tables on which the facts depend. Such a model is called a star
schema because of the shape of the model representation. A simple example of such a
schema is shown in Figure 7.5 for a university where we assume that the number of
students is given by the four dimensions degree, year, country and scholarship. These
four dimensions were chosen because we are interested in finding out how many students
come to each degree program, each year, from each country under each scholarship

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A characteristic of a star schema is that all the dimensions directly link to the fact table.
The fact table may look like table 7.1 and the dimension tables may look Tables 7.2 to

Table 7.1 An example of the fact table

Year Degree name Country name Scholarship name Number
200301 BSc Australia Govt 35
199902 MBBS Canada None 50
200002 LLB USA ABC 22
199901 BCom UK Commonwealth 7
200102 LLB Australia Equity 2

The first dimension is the degree dimension. An example of this dimension table is
Table 7.2.

Table 7.2 An example of the degree dimension table

Name Faculty Scholarship eligibility Number of semesters
BSc Science Yes 6
MBBS Medicine No 10
LLB Law Yes 8
BCom Business No 6
LLB Arts No 6

We now present the second dimension, the country dimension. An example of this
dimension table is Table 7.3.
Table 7.3 An example of the country dimension table

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Name Continent Education Level Major
Nepal Asia Low Hinduism
Indonesia Asia Low Islam
Norway Europe High Christianity
Singapore Asia High NULL
Colombia South America Low Christianity

The third dimension is the scholarship dimension. The dimension table is given in Table

Table 7.4 An example of the scholarship dimension table

Name Amount (%) Scholarship eligibility Number
Colombo 100 All 6
Equity 100 Low income 10
Asia 50 Top 5% 8
Merit 75 Top 5% 5
Bursary 25 Low income 12

The fourth dimension is the year dimension. The dimension table is given in Table 7.5.

Table 7.5 An example of the year dimension table

Name New programs

2001 Journalism
2002 Multimedia

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2003 Biotechnology

We now present further examples of the star schema. Figure 7.7 shows a star
schema for a model with four dimensions.
Star schema may be refined into snowflake schemas if we wish to provide support
for dimension hierarchies by allowing the dimension tables to have subtables to represent
the hierarchies. For example, Figure 7.8 shows a simple snowflake schema for a two-
dimensional example.

Degree Country

Name Name

Degree Name Continent


Country Education
Scholarship Name Level

Number of
Number of students Major
Semesters religion

Figure 7.6 Star schema for a two-dimensional example.

The star and snowflake schemas are intuitive, easy to understand, can deal with aggregate
data and can be easily extended by adding new attributes or new dimensions. They are
the popular modeling techniques for a data warehouse. Entry-relationship modeling is
often not discussed in the context of data warehousing although it is quite straightforward
to look at the star schema as an ER model. Each dimension may be considered an entity
and the fact may be considered either a relationship between the dimension entities or an

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entity in which the primary key is the combination of the foreign keys that refer to the


Name Name

Degree Name Continent


Country Education
Scholarship Name Level
Number of name Major
Semesters religion

Number of
Revenue Name

Last year

Figure 7.7 Star schema for a four-dimensional example.

the popular modeling techniques for a data warehouse. Entity-relationship modeling is

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to look at the star schema as an ER model.


Number of Degree Name


Number of


Faculty Amount

Eligibility Eligibility

Number of

Figure 1.8 An example of a snowflake schema.

The dimensional structure of the star schema is called a multidimensional cube in

online analytical processing (OALP). The cubes may be precomputed to provide very
quick response to management OLAP queries regardless of the size of the data

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Implementation steps

1. Requirements analysis and capacity planning: In other projects, the first step in
data warehousing involves defining enterprise needs, defining architecture,
carrying out capacity planning and selecting the hardware and software tools.
This step will involve consulting senior management as well as the various
2. Hardware integration: Once the hardware and software have been selected, they
need to be put together by integrating the servers, the storage devices and the
client software tools.
3. Modelling: Modelling is a major step that involves designing the warehouse
schema and views. This may involve using a modelling tool if the data
warehouse is complex.
4. Physical modelling: For the data warehouse to perform efficiently, physical
modelling is required. This involves designing the physical data warehouse
organization, data placement, data partitioning, deciding on access methods and
5. Sources: The data for the data warehouse is likely to come from a number of
data sources. This step involves identifying and connecting the sources using
gateways, ODBC drives or other wrappers.
6. ETL: The data from the source systems will need to go through an ETL process.
The step of designing and implementing the ETL process may involve
identifying a suitable ETL tool vendor and purchasing and implementing the tool.
This may include customizing the tool to suit the needs of the enterprise.
7. Populate the data warehouse: Once the ETL tools have been agreed upon,
testing the tools will be required, perhaps using a staging area. Once everything is

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working satisfactorily, the ETL tools may be used in populating the warehouse
given the schema and view definitions.
8. User applications: For the data warehouse to be useful there must be end-user
applications. This step involves designing and implementing applications
required by the end users.
9. Roll-out the warehouse and applications: Once the data warehouse has been
populated and the end-user applications tested, the warehouse system and the
applications may be rolled out for the user community to use.

Implementation Guidelines

1. Build incrementally: Data warehouses must be built incrementally. Generally it

is recommended that a data mart may first be built with one particular project in
mind and once it is implemented a number of other sections of the enterprise may
also wish to implement similar systems. An enterprise data warehouse can then
be implemented in an iterative manner allowing all data marts to extract
information from the data warehouse. Data warehouse modelling itself
is an iterative methodology as users become familiar with the technology and are
then able to understand and express their requirements more clearly.
2. Need a champion: A data warehouse project must have a champion who is
willing to carry out considerable research into expected costs and benefits of the
project. Data warehousing projects require inputs from many units in am
enterprise and therefore need to be driven by someone who is capable of
interaction with people in the enterprise and can actively persuade colleagues.
Without the cooperation of other units, the data model for the warehouse and the
data required to populate the warehouse may be more complicated than they
need to be. Studies have shown that having a champion can help adoption and
of data warehousing projects.
3. Senior management support: A data warehouse project must be fully supported
by the senior management. Given the resource intensive nature of such projects
and the time they can take to implement, a warehouse project calls for a sustained
commitment from senior management. This can sometimes be difficult since it
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may be hard to quantify the benefits of data warehouse technology and the
managers may consider it a cost without any explicit return on investment. Data
warehousing project studies show that top management support is essential for
the success of a data warehousing project.
4. Ensure quality: Only data that has been cleaned and is of a quality that is
understood by the organization should be loaded in the data warehouse. The data
quality in the source systems is not always high and often little effort is made to
improve data quality in the source systems. Improved data quality, when
recognized by senior managers and stakeholders, is likely to lead to improved
support for a data warehouse project.
5. Corporate strategy: A data warehouse project must fit with corporate strategy
and business objectives. The objectives of the project must be clearly defined
before the start of the project. Given the importance of senior management
support for a data warehousing project, the fitness of the project with the
corporate strategy is essential.
6. Business plan: The financial costs (hardware, software, peopleware), expected
benefits and a project plan (including an ETL plan) for a data warehouse project
must be clearly outlined and understood by all stakeholders. Without such
understanding, rumours about expenditure and benefits can become the only
source of information, undermining the project.
7. Training: A data warehouse project must not overlook data warehouse training
requirements. For a data warehouse project to be successful, the users must be
trained to use the warehouse and to understand its capabilities. Training of users
and professional development of the project team may also be required since data
warehousing is a complex task and the skills of the project team are critical to the
success of the project.
8. Adaptability: The project should build in adaptability so that changes may be
made to the data warehouse if and when required. Like any system, a data
warehouse will need to change, as needs of an enterprise change. Furthermore,
once the data warehouse is operational, new applications using the data

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warehouse are almost certain to be proposed. The system should be able to

support such new applications.
9. Joint management: The project must be managed by both IT and business
professionals in the enterprise. To ensure good communication with the
stakeholders and that the project is focused on assisting the enterprises business,
business professionals must be involved in the project along with technical


Given the complexity of information in an ODS and the data warehouse, it is essential
that there be a mechanism for users to easily find out what data is there and how it can be
used to meet their needs. metadata about the ODS or the data warehouse
achieves this. Metadata is data about data or documentation about the data that is needed
by the users. Another description of metadata is that it is structured data which describes
the characteristics of a resource. Metadata is stored in the system itself and can be
queried using tools that are available on the system.
Several examples of metadata that should be familiar to the reader:

1. A library catalogue may be considered metadata. The catalogue metadata consists

of a number of predefined elements representing specific attributes of a
resource, and each element can have one or more values. These elements could
be the name of the author, the name of the document, the publishers name, the
publication date and the category to which it belongs. They could even include
an abstract of
the data.
2. The table of contents and the index in a book may be considered metadata for the
3. Suppose we say that a data element about a person is 80. This must then be
described by noting that it is the persons weight and the unit is kilograms.
Therefore (weight, kilogram) is the metadata about the data 80.
4. Yet another example of metadata is data about the tables and figures in a
document like this book. A table (which is data) has a name (e.g. table titles in

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this chapter) and there are column names of the table that may be considered
metadata. The figures also have titles or names.

There are many metadata standards. For example, the AGLS (Australian
Government Locator Service) Metadata standard is a set of 19 descriptive elements which
Australian government departments and agencies can use to improve the visibility and
accessibility of their services and information over the Internet.
In a database, metadata usually consists of table (relation) lists, primary key
names, attributes names, their domains, schemas, record counts and perhaps a list of the
most common queries. Additional information may be provided including logical and
physical data structures and when and what data was loaded.
In the context of a data warehouse, metadata has been defined as all of the
information in the data warehouse environment that is not the actual data itself.
In the data warehouse, metadata needs to be much more comprehensive. It may be
classified into two groups: back room metadata and front room metadata. Much
important information is included in the back room metadata that is process related and
guides, for example, the ETL processes.

ODS Software

IQ Solutions: Dynamic ODS from Sybase offloads data from OLTP systems and
makes if available on a Sybase IQ platform for queries and analysis.

ADH Active Data Hub from Glenridge Solutions is a real-time data integration
and reporting solution for PeopleSoft, Oracle and SAP databases. ADH includes
an ODS, an enterprise data warehouse, a workflow initiator and a meta library.
ZLE Software

HP ZLE framework based on the HP NonStop platform combines application and data
integration to create an enterprise-wide solution for real-time information. The ZLE
solution is targeted at retail, telecommunications, healthcare, government and finance.

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ETL Software

Aradyme Data Services from Aradym Corporation provides data migration

services for extraction, cleaning, transformation and loading from any source to
any destination. Aradyme claims to minimize the risks inherent in many-to-one,
many-to-many and similar migration projects.

DataFlux from a company with the same name (acquired by SAS in 2000)
provides solutions that help inspect, correct, integrate, enhance, and control data.
Solutions include data

Dataset V from Intercon Systems Inc is an integrated suite for data cleaning,
matching, positive identification, de-duplication and statistical analysis.

WinPure List Cleaner Pro from WinPure provides a suite consisting of eight
modules that clean, correct unwanted punctuation and spelling errors, identify
missing data via graphs and a scoring system and removes duplicates from a
variety of data sources.

Data Warehousing Software

mySAP Business Intelligence provides facilities of ETL, data warehouse

management and business modelling to help build data warehouse, model
information architecture and manage data from multiple sources.

SQL Server 2005 from Microsoft provides ETL tools as well as tools for
building a relational data warehouse and a multidimensional database.

Sybase IQ is designed for reporting, data warehousing and analytics. It claims to

deliver high query performance and storage efficiency for structured and
unstructured data. Sybase has partnered with Sun in providing data warehousing

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A dimension is an attribute or an ordinate within a multidimensional structure

consisting of a list of values (members). For example, the degree, the country, the
scholarship and the year were the four dimensions used in the student database.
Dimensions are used for selecting and aggregating data at the desired level. A dimension
does not include ordering of values, for example there is no ordering associated with
values of each of the four dimensions, but a dimension may have one or more
hierarchies that show parent /child relationship between the members of a dimension.

For example, the dimension country may have a hierarchy that divides the world into
continents and continents into regions followed by regions into countries if such a
hierarchy is useful for the applications. Multiple hierarchies may be defined on a
dimension. For example, counties may be defined to have a geographical hierarchy and
may have another hierarchy defined on the basis of their wealth or per capita income (e.g.
high, medium, low).

The non-null values of facts are the numerical values stored in each data cube cell. They
are called measures. A measure is a non-key attribute in a fact table and the value of the
measure is dependent on the values of the dimensions. Each unique combination of

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members in a Cartesian product dimensions of the cube identifies precisely one data cell
within the cube and that cell stores the values of the measures.
The SQL command GROUP BY is unusual aggregation operator in that a table is divided
into sub-tables based on the attribute values in the GROUP BY clause so that each sub-
table has the same values for the attribute and then aggregations over each sub-table are
carried out. SQL has a variety of aggregation functions including max, min, average,
count which are used by employing the GROUP BY facility.

A data cube computes aggregates overall subsets of dimensions specified in the cube. A
cube may be found at the union of (large) number of SQL GROUP-BY operations.
Generally, all or some of the aggregates are pre-computed to improve query response
time. A decision has to be made as to what and how much should be pre-computed since
pre-computed queries require storage and time to compute them.

A data cube is often implemented as a database in which there are dimension tables each
of which provides details of a dimension. The database may be the enterprise data

2.2 OLAP

OLAP systems are data warehouse front-end software tools to make aggregate
data available efficiently, for advanced analysis, to managers of an enterprise. The
analysis often requires resource intensive aggregations processing and therefore it
becomes necessary to implement a special database (e.g. data warehouse) to improve
OLAP response time. It is essential that an OLAP system provides facilities for a
manager to pose ad hoc complex queries to obtain the information that he/she requires.

Another term that is being used increasingly is business intelligence. It is used to

mean both data warehousing and OLAP. It has been defined as a user-centered process of
making. Normally this involves a process of accessing data (usually stored within the
data warehouse) and analyzing it to draw conclusions and derive insights with the

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purpose of effecting positive change within an enterprise. Business intelligence is closely

related to OLAP.

A data warehouse and OLAP are based on a multidimensional conceptual view of

the enterprise data. Any enterprise data is multidimensional consisting of dimensions
degree, country, scholarship, and year. Data that is arranged by the dimensions is like a
spreadsheet, although a spreadsheet presents only two-dimensional data with each cell
containing an aggregation. As an example, table 8.1 shows one such two-dimensional
spreadsheet with dimensions Degree and Country, where the measure is the number of
students joining a university in a particular year or semester.

Table 8.1 A multidimensional view of data for two dimensions



Australia 5 20 15 50 11 101
India 10 0 15 25 17 67
Malaysia 5 1 10 12 23 51
Singapore 2 2 10 10 31 55
Sweden 5 0 5 25 7 42
UK 5 15 20 20 13 73
USA 0 2 20 15 19 56
ALL 32 40 95 157 121 445

Table 8.1 be the information for the year 2001. Similar spreadsheet views would be
available for other years. Three-dimensional data can also be organized in a spreadsheet
using a number of sheets or by using a number of two-dimensional tables in the same

Although it is useful to think of OLAP systems as a generalization of

spreadsheets, spreadsheets are not really suitable for OLAP in spite of the nice user-
friendly interface that they provide. Spreadsheets tie data storage too tightly to the

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presentation. It is therefore difficult to obtain other desirable views of the information.

Furthermore it is not possible to query spreadsheets. Also, spreadsheets become unwieldy
when more than three dimensions are to be represented. It is difficult to imagine a
spreadsheet with millions of rows or with thousands of formulas. Even with small
spreadsheets, formulas often have errors. An error-free spreadsheet with thousands of
formulas would therefore be very difficult to build. Data cubes essentially generalize
spreadsheets to any number of dimensions.

OLAP is the dynamic enterprise analysis required to create, manipulate, animate

and synthesize information from exegetical, contemplative and formulaic data analysis

Essentially what this definition means is that the information is manipulated from the
point if view of a manager (exegetical), from the point of view of someone who has
thought about it(contemplative) and according to some formula(formulaic).

Another definition of OLAP, which is software technology that enables analysts,

access to a wide variety of possible views of information that, has been transformed from
raw data to reflect that real dimensional of the enterprise as understood by the user.

An even simpler definition is that OLAP is a fast analysis of shared

multidimensional information for advanced analysis. This definition (sometimes called
FASMI) implies that most OLAP queries should be answered within seconds.
Furthermore, it is expected that most OLAP queries can be answered without any

In summary, a manager would want even the most complex query to be answered
quickly; OLAP is usually a multi-user system that may be run on a separate server using
specialized OLAP software. The major OLAP applications are trend analysis over a

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number of time periods, slicing, dicing, drill-down and roll-up to look at different levels
of detail and pivoting or rotating to obtain a new multidimensional view.


The following are the differences between OLAP and OLTP systems.
1. Users: OLTP systems are designed for office workers while the OLAP systems are
designed for decision makers. Therefore while an OLTP system may be accessed by
hundreds or even thousands of users in a large enterprise, an OLAP system is likely to be
accessed only by a select group of managers and may be used only by dozens of users.

2. Functions: OLTP systems are mission-critical. They support day-to-day operations of

an enterprise and are mostly performance and availability driven. These systems carry out
simple repetitive operations. OLAP systems are management-critical to support decision
of an enterprise support functions using analytical investigations. They are more
functionality driven. These are ad hoc and often much more complex operations.

3. Nature: Although SQL queries often return a set of records, OLTP systems are
designed to process one record at a time, for example a record related to the customer
who might be on the phone or in the store. OLAP systems are not designed to deal with
individual customer records. Instead they involve queries that deal with many records at a
time and provide summary or aggregate data to a manager. OLAP applications involve
data stored in a data warehouse that has been extracted from many tables and perhaps
from more than one enterprise database.

4. Design: OLTP database systems are designed to be application-oriented while OLAP

systems are designed to be subject-oriented. OLTP systems view the enterprise data as a
collection of tables (perhaps based on an entity-relationship model). OLAP systems view
enterprise information as multidimensional).

5. Data: OLTP systems normally deal only with the current status of information. For
example, information about an employee who left three years ago may not be available

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on the Human Resources System. The old information may have been achieved on some
type of stable storage media and may not be accessible online. On the other hand, OLAP
systems require historical data over several years since trends are often important in
decision making.

6. Kind of use: OLTP systems are used for reading and writing operations while OLAP
systems normally do not update the data.
The differences between OLTP and OLAP systems are:

Property OLTP OLAP

Nature of users Operations workers Decision makers
Functions Mission-critical Management-critical
Nature of queries Mostly simple Mostly complex
Nature of usage Mostly repetitive Mostly ad hoc
Nature of design Application oriented Subject oriented
Number of users Thousands Dozens
Nature of data Current, detailed, relational Historical, summarized,
Updates All the time Usually not allowed
Table 8.1 Comparison of OLTP and OLAP system

FASMI Characteristics

In the FASMI characteristics of OLAP systems, the name derived from the first letters of
the characteristics are:
Fast: As noted earlier, most OLAP queries should be answered very quickly,
perhaps within seconds. The performance of an OLAP system has to be like that of a
search engine. If the response takes more than say 20 seconds, the user is likely to move
away to something else assuming there is a problem with the query. Achieving such
performance is difficult. The data structures must be efficient. The hardware must be
powerful enough for the amount of data and the number of users. Full pre-computation of
aggregates helps but is often not practical due to the large number of aggregates. One

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approach is to pre-compute the most commonly queried aggregates and compute the
remaining on-the-fly.
Analytic: An OLAP system must provide rich analytic functionality and it is
expected that most OLAP queries can be answered without any programming. The
system should be able to cope with any relevant queries for the application and the user.
Often the analysis will be using the vendors own tools although OLAP software
capabilities differ widely between products in the market.
Shared: An OLAP system is shared resource although it is unlikely to be
shared by hundreds of users. An OLAP system is likely to be accessed only by a select
group of managers and may be used merely by dozens of users. Being a shared system,
system should be provide adequate security for confidentiality as well as integrity.
Multidimensional: This is the basic requirement. Whatever OLAP software is
being used, it must provide a multidimensional conceptual view of the data. It is because
of the multidimensional view of data that we often refer to the data as a cube. A
dimension often has hierarchies that show parent / child relationships between the
members of a dimension. The multidimensional structure should allow such hierarchies.
Information: OLAP systems usually obtain information from a data warehouse.
The system should be able to handle a large amount of input data. The capacity of an
OLAP system to handle information and its integration with the data warehouse may be

Codds OLAP Characteristics

Codd et als 1993 paper listed 12 characteristics (or rules) OLAP systems. Another six in
1995 followed these. Codd restructured the 18 rules into four groups. These rules provide
another point of view on what constitutes an OLAP system.
All the 18 rules are available at Here we
discuss 10 characteristics, that are most important.

1. Multidimensional conceptual view: As noted above, this is central characteristic of an

OLAP system. By requiring a multidimensional view, it is possible to carry out
operations like slice and dice.
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2. Accessibility (OLAP as a mediator): The OLAP software should be sitting between

data sources (e.g data warehouse) and an OLAP front-end.
3. Batch extraction vs interpretive: An OLAP system should provide multidimensional
data staging plus precalculation of aggregates in large multidimensional databases.
4. Multi-user support: Since the OLAP system is shared, the OLAP software should
provide many normal database operations including retrieval, update, concurrency
control, integrity and security.
5. Storing OLAP results: OLAP results data should be kept separate from source data.
Read-write OLAP applications should not be implemented directly on live transaction
data if OLAP source systems are supplying information to the OLAP system directly.
6. Extraction of missing values: The OLAP system should distinguish missing values
from zero values. A large data cube may have a large number of zeros as well as some
missing values. If a distinction is not made between zero values and missing values, the
aggregates are likely to be computed incorrectly.
7. Treatment of missing values: An OLAP system should ignore all missing values
regardless of their source. Correct aggregate values will be computed once the missing
values are ignored.
8. Uniform reporting performance: Increasing the number of dimensions or database
size should not significantly degrade the reporting performance of the OLAP system.
This is a good objective although it may be difficult to achieve in practice.
9. Generic dimensionality: An OLAP system should treat each dimension as equivalent
in both is structure and operational capabilities. Additional operational capabilities may
be granted to selected dimensions but such additional functions should be grantable to
any dimension.
10. Unlimited dimensions and aggregation levels: An OLAP system should allow
unlimited dimensions and aggregation levels. In practice, the number of dimensions is
rarely more than 10 and the number of hierarchies rarely more than six.


1. Understanding and improving sales: For an enterprise that has many products
and uses a number of channels for selling the products, OLAP can assist in

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finding the most popular products and the most popular channels. In some cases it
may be possible to find the most profitable customers. For example, considering
the telecommunications industry and only considering one product,
communication minutes, there is a large amount of data if a company wanted to
analyze the sales of product for every hour of the day (24 hours), differentiate
between weekdays and weekends (2 values) and divide regions to which calls are
made into 50 regions.

2. Understanding and reducing costs of doing business: Improving sales is one

aspect of improving a business, the other aspect is to analyze costs and to control
them as much as possible without affecting sales. OLAP can assist in analyzing
the costs associated with sales. In some cases, it may also be possible to identify
expenditures that produce a high return on investment (ROI). For example,
recruiting a top salesperson may involve significant costs, but the revenue
generated by the salesperson may justify the investment.


V-C, Deans

Department & Faculty

Management, Heads

Daily operations Registrar,

HR, Finance

Figure 2.1 A typical University management hierarchy

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The multidimensional view of data is in some ways natural view of any enterprise of
managers. The triangle diagram in Figure 8.1 shows that as we go higher in the triangle
hierarchy the managers need for detailed information declines.
The multidimensional view of data by using an example of a simple OLTP
database consists of the three tables. Much of the literature on OLAP uses examples of a
shoe store selling different colour shoes of different styles.
It should be noted that the relation enrolment would normally not be required
since the degree a student is enrolled in could be included in the relation student but some
students are enrolled in double degrees and so the relation between the student and the
degree is multifold and hence the need for the relation enrolment.

student(Student_id, Student_name, Country, DOB, Address)

enrolment(Student_id, Degree_id, SSemester)
degree(Degree_id, Degree_name, Degree_length, Fee, Department)

An example of the first relation, i.e. student, is given in Table 2.2

Student_id Student_name Country DOB Address

8656789 Peta Williams Australia 1/1/1980 Davis Hall
8700020 John Smith Canada 2/2/1981 9 Davis Hall
8900020 Arun Krishna USA 3/3/1983 90 Second Hall
8801234 Peter Chew UK 4/4/1983 88 Long Hall
8654321 Reena Rani Australia 5/5/1984 88 Long Hall
8712374 Kathy Garcia Malaysia 6/6/1980 88 Long Hall
8612345 Chris Watanabe Singapore 7/7/1981 11 Main street
8744223 Lars Anderssen Sweden 8/8/1982 Null
8977665 Sachin Singh UAE 9/9/1983 Null
9234567 Rahul Kumar India 10/10/1984 Null
9176543 Saurav Gupta UK 11/11/1985 1, Captain Drive

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Table 8.3 presents an example of the relation enrolment. In this table, the attribute
SSemester in the semester in which the student started the current degree. We code it by
using the year followed by 01 for the first semester and 02 for the second. We
assume that new students are admitted in each semester. Table 8.4 is an example of the
relation degree. In this table, the degree length is given in terms of the number of
semester it normally takes to finish it. The fee is assumed to be in thousands of dollars per
Table 2.3 The relation enrolment

Student_id Degree_id SSemester

8900020 1256 2002-01
8700074 3271 2002-01
8700074 3321 2002-02
8900020 4444 2000-01
8801234 1256 2000-01
8801234 3321 1999-02
8801234 3333 1999-02
8977665 3333 2000-02

Table 2.4 The relation degree

Degree_id Degree_name Degree_length Fee Department

1256 BIT 6 18 Computer Sci.
2345 BSc 6 20 Computer Sci
4325 BSc 6 20 Chemistry
3271 BSc 6 20 Physics
3321 BCom 6 16 Business
4444 MBBS 12 30 Medicine
3333 LLB 8 22 Law

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It is clear that the information given in Tables 8.2, 8.3 and 8.4, although suitable for a
student enrolment OLTP system, is not suitable for efficient management decision
making. The managers do not need information about the individual students, the degree
they are enrolled in, and the semester they joined the university. What the managers need
is the trends in student numbers in different degree programs and from different

We first consider only two dimensions. Let us say we are primarily interested in finding
out how many students from each country came to do a particular degree. Therefore we
may visualize the data as two-dimensional, i.e.,
Country x Degree
A table that summarizes this type of information may be represented by a two-
dimensional spreadsheet given in Table 8.5 (the numbers in Table 8.5 do not need relate
to the numbers in Table 8.3). We may call that this table gives the number of students
admitted (in say, 2000-01) a two-dimensional cube.

Table 2.5 A two-dimensional table of aggregates for semester 2000-01

Country \ Degree BSc LLB MBBS BCom BIT ALL

Australia 5 20 15 50 11 101
India 10 0 15 25 17 67

Malaysia 5 1 10 12 23 51
Singapore 2 2 10 10 31 55
Sweden 5 0 5 25 7 42
UK 5 15 20 20 13 73
USA 0 2 20 15 19 56
ALL 32 40 95 157 121 445

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Using this two-dimensional view we are able to find the number of students joining any
degree from any country (only for semester 2000-01). Other queries that we are quickly
able to answer are:

How many students started BIT in 2000-01?

How many students joined from Singapore in 2000-01?

The data given in Table 8.6 is for a particular semester, 2000-01. A similar table would
be available for other semesters. Let us assume that the data for 2001-01 is given in Table

Table 2.6 A two-dimensional table of aggregates for semester 2001-01

Country \ Degree BSc LLB MBBS BCom BIT ALL

Australia 7 10 16 53 10 96
India 9 0 17 22 13 61
Malaysia 5 1 19 19 20 64
Singapore 2 2 10 12 23 49
Sweden 8 0 5 16 7 36
UK 4 13 20 26 11 74
USA 4 2 10 10 12 38
ALL 39 28 158 158 96 418

Let us now imagine that Table 8.6 is put on top of Table 8.5. We now have a three-
dimensional cube with SSemester as the vertical dimension. We now put on top of these
two tables another table that gives the vertical sums, as shown in Table 8.7.

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Table 2.7 Two-dimensional table of aggregates for both semesters

Country \ Degree BSc LLB MBBS BCom BIT ALL

Australia 12 30 31 103 21 197
India 19 0 32 47 30 128
Malaysia 10 2 29 31 43 115
Singapore 4 4 20 22 54 104
Sweden 13 0 10 41 14 78
UK 9 28 40 46 24 147
USA 4 4 30 25 31 94
ALL 71 68 192 315 217 863

Tables 8.5, 8.6 and 8.7 together now form a three-dimensional cube. The table 8.7
provides totals for the two semesters and we are able to drill-down to find numbers in
individual semesters. Note that a cube does not need to have an equal number of
members in each dimension. Putting the three tables together gives a cube of 8 x 6 x 3 ( =
144) cells including the totals along every dimension.

A cube could be represented by:

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Country x Degree x Semester

Figure 2.2 The cube formed by Tables 8.6, 8.7 and 8.8

In the three-dimensional cube, the following eight types of aggregations or queries are
1. null (e.g. how many students are there? Only 1 possible query)
2. degrees (e.g. how many students are doing BSc? 5 possible queries if we assume
5 different degrees)

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3. semester (e.g. how many students entered in semester 2000-01? 2 possible queries
if we only have data about 2 semesters)
4. country (e.g. how many students are from the USA? 7 possible queries if there are
7 countries)
5. degrees, semester (e.g. how many students entered in 2000-01 to enroll in BCom?
With 5 degrees and 2 different semesters 10 queries)
6. (ALL, b, c) semester, country (e.g. how many students from the UK entered in
2000-01? 14 queries)
7. (a, b, ALL) degrees, country (e.g. how many students from Singapore are enrolled
in BCom? 35 queries)
8. (a, b, c) all (e.g. how many students from Malaysia entered in 2000-01 to enroll in
BCom? 70 queries)


1. Pre-compute and store all: This means that millions of aggregates will need to be
computed and stored. Although this is the best solution as far as query response
time is concerned, the solution is impractical since resources required to compute
the aggregates and to store them will be prohibitively large for a large data cube.
Indexing large amounts of data is also expensive.

2. Pre-compute (and store) none: This means that the aggregates are computed on-
the-fly using the raw data whenever a query is posed. This approach does not
require additional space for storing the cube but the query response time is likely
to be very poor for large data cubes.

3. Pre-compute and store some: This means that we pre-compute and store the
most frequently queried aggregates and compute others as the need arises. We
may also be able to derive some of the remaining aggregates using the aggregates
that have been computed. It may therefore be worthwhile also to pre-
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compute some aggregates that are not most frequently queried but help in deriving
many other aggregates. It will of course not be possible to derive all the
aggregates from the pre-computed aggregates and it will be necessary to access
the database (e.g the data warehouse) to compute the remaining aggregates. The
more aggregates we are able to pre-compute the better the query performance.

It can be shown that large numbers of cells do have an ALL value and may therefore be
derived from other aggregates. Let us reproduce the list of queries we had and define
them as (a, b, c) where a stands for a value of the degree dimension, b for country and c
for starting semester:

1. (ALL, ALL, ALL) null (e.g. how many students are there? Only 1 query)
2. (a, ALL, ALL) degrees (e.g. how many students are doing BSc? 5 queries)
3. (ALL, ALL, c) semester (e.g. how many students entered in semester 2000-01? 2
4. (ALL, b, ALL) country (e.g. how many students are from the USA? 7 queries)
5. (a, ALL, c) degrees, semester (e.g. how many students entered in 2000-01 to
enroll in BCom? 10 queries)
2000-01? 14 queries)
in BCom? 35 queries)
BCom? 70 queries)

It is therefore possible to derive the other 74 of the 144 queries from the last 70
queries of type (a, b, c). Of course in a very large data cube, it may not be practical
even to pre-compute all the (a, b, c) queries and decision will need to be made which
ones should be pre-computed given that storage availability may be limited and it
may be required to minimize the average query cost.

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In Figure 8.3 we show how the aggregated above are related and how an aggregate at the
higher level may be computed from the aggregates below. For example, aggregates
(ALL, ALL, c) may be derived from either (a, ALL, c) by summing over all a values
from (ALL, b, c) by summing over all b values.




a, ALL, c ALL, b, c a, b, ALL

a, b, c

Figure 2.3 Relationships between aggregations of a three-dimensional cube

Another related issue is where the data used by OLAP will reside. We assume that the
data is stored in a data warehouse or in one or more data marts.

Data cube products use different techniques for pre-computing aggregates and
storing them. They are generally based on one of two implementation models. The first
model, supported by vendors of traditional relational model databases, is called the
ROLAP model or the Relational OLAP model. The second model is called the MOLAP
model for multidimensional OLAP. The MLOAP model provides a direct

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multidimensional view of the data whereas the RLOAP model provides a relational view
of the multidimensional data in the form of a fact table.


ROLAP uses a relational DBMS to implement an OLAP environment. It may be

considered a bottom-up approach which is typically based on using a data warehouse that
has been designed using a star schema. The data therefore is likely to be in a
denormalized structure. A normalized database avoids redundancy but is usually not
appropriate for high performance. The summary data will be held in aggregate tables.
The data warehouse provides the multidimensional capabilities by representing data in
fact table(s) and dimension tables. The fact table contains one column for each dimension
and one column for each measure and every row of the table [rovides one fact. A fact
then is represented as (BSc, India, 2001-01) with the last column as 30. An OLAP tool is
then provided to manipulate the data in these data warehouse tables. This tool essentially
groups the fact table to find aggregates and uses some of the aggregates already
computed to find new aggregates.

The advantage of using ROLAP is that it is more easily used with existing relational
DBMS and the data can be stored efficiently using tables since no zero facts need to be
stored. The disadvantage of the ROLAP model is its poor query performance. Proponents
of the MLOAP model have called the ROLAP model SLOWLAP. Some products in this
category are Oracle OLAP mode, OLAP Discoverer, MicroStrategy and Microsoft
Analysis Services.


MOLAP is based on using a multidimensional DBMS rather than a data warehouse to

store and access data. It may be considered as a top-down approach to OLAP. The
multidimensional database systems do not have a standard approach to storing and
maintaining their data. They often use special-purpose file systems or indexes that store
pre-computation of all aggregations in the cube. For example, in ROLAP a cell was

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represented as (BSc, India, 2001-01) with a value 30 stored in the last column. In
MOLAP, the same information is stored as 30 and the storage location implicitly gives
the values of the dimensions. The dimension values do not need to be stored since all the
values of the cube could be stored in an array in a predefined way. For example, the cube
in Figure 8.2 may be represented as an array like the following:

12 30 31 10 21 19 19 0 32 47 30 12 10 2 29 31 43
3 7 8

If the values of the dimensions are known, we can infer the cell location in the array. If
the cell location is known, the values of the dimension may be inferred. This is obviously
a very compact notation for storing a multidimensional data cube although a couple of
problems remain. Firstly the array is likely to be too large to be stored in the main
memory. Secondly, this representation does not solve the problem of efficiently
representing sparse cubes. To overcome the problem of the array being too large for main
memory, the array may be split into pieces called chunks, each of which is small
enough to fit in the main memory. To overcome the problem of sparseness, the chunks
may be compressed.

MOLAP systems have to deal with sparsity since a very percentage of the cells can be
empty in some applications. The MOLAP implementation is usually exceptionally
efficient. The disadvantage of using MOLAP is that it is likely to be more expensive than
OLAP, the data is not always current, and it may be more difficult to scale a MOLAP
system for very large OLAP problems. Some MOLAP products are Hyperion Essbase
and Applix iTM1. Oracle and Microsoft are also competing in this segment of the OLAP

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The differences between ROLAP and MOLAP are summarized in Table 8.8

Table 2.8 Comparison of MOLAP and ROLAP


Data structure Multidimensional database Relational tables (each cell is a row)
using sparse arrays
Disk space Separate database for data May not require any space other than
cube; large for large data that available in the data warehouse
Retrieval Fast(pre-computed) Slow(computes on-the-fly)
Scalability Limited (cubes can be very Excellent
Best suited for Inexperienced users, limited Experienced users, queries change
set of queries frequently
DBMS Usually weak Usually very strong


A number of operations may be applied to data cubes. The common ones are:
Slice and dice

Roll-up is like zooming out on the data cube. It is required when the user needs further
abstraction or less detail. This operation performs further aggregations on the data, for
example, from single degree programs to all programs offered by a School or department,
from single countries to a collection of countries, and from individual semesters to

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academic years. Often a hierarchy defined on a dimension is useful in the roll-up

operation as suggested by the example of countries and regions.

We provide an example of roll-up based on Table =s 8.6, 8.7 and 8.8. We first
define hierarchies on two dimensions. Amongst countries, let us define:
1. Asia (India, Malaysia, Singapore)
2. Europe (Sweden, UK)
3. Rest (Australia, USA)
Another hierarchy is defined on the dimension degree:
1. Science (BSc, BIT)
2. Medicine (MBBS)
3. Business and Law (BCom, LLB)
The result of a roll-up for both semesters together from Table 8.8 then is given in Table

Table 2.9 Result of a roll-up operation using Table 8.7

Country \ Degree Science Medicine Business and Law

Asia 160 81 106
Europe 60 50 115
Rest 68 61 162

Drill-down is like zooming in on the data and is therefore the reverse of roll-up. It is an
appropriate operation when the user needs further details or when the user wants to
partition more finely or wants to focus on some particular values of certain dimensions.
Drill-down adds more details to the data. Hierarchy defined on a dimension may be
involved in drill-down. For example, a higher level views of student data, for example in
Table 8.9, gives student numbers for the two semesters for groups of countries and
groups of degrees. If one is interested in more detail then it is possible to drill-down to
tables 8.6 and 8.7 for student numbers in each of the semesters for each country and for
each degree.

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Slice and dice

Slice and dice are operations for browsing the data in the cube. The terms refer to the
ability to look at information from different viewpoints.
A slice is a subset of the cube corresponding to a single value for one or more
members of the dimensions. For example, a slice operation is performed when the user
wants a selection on one dimension of a three-dimensional cube resulting in a two-
dimensional site. Let the degree dimension be fixed as degree = BIT. The slice will not
include any information about other degrees. The information retrieved therefore is more
like a two-dimensional cube for degree = BIT as shown in Table 8.10.

Table 2.10 Result of a slice when degree value is BIT

Country \ Semester 2000-01 2000-02 2001-01 2001-02

Australia 11 5 10 2
India 17 0 13 5
Malaysia 23 2 20 1
Singapore 31 4 23 2
Sweden 7 0 7 4
UK 13 8 11 6
USA 19 4 12 5

It should be noted that Table 8.7 also is a slice (with SSemester = 2000-01) from the
cube built by piling several tables like Tables 8.7 and 8.8 about different semesters on top
of each other. It is shown in Figure 8.4.

The dice operation is similar to slice but dicing does not involve reducing the number of
dimensions. A dice is obtained by performing a selection on two or more dimensions. For
example, one may only be interested in degrees BIT and BCom and countries Australia,
India, and Malaysia for semesters 2000-01 and 2000-01. The result is a three-dimensional
cube and we show it by Table 8.11 and 8.12 placed on top of each other. For example one
may only be interested in the degrees BIT and BCom and the countries, Australia, India

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and Malaysia for semesters 2000-01 and 2001-01. The result is a three-dimensional cube
as shown in Figure 8.4 and we show it by Tables 8.11 and 8.12 for the two semesters
placed on top of each other. We have left out the table that shows the totals from these

Figure 2.4 A slice from the cube in Figure 8.2

Table 2.11 A three-dimensional dice from a three-dimensional cube (SSemester 2000-


Country \ Degree BCom BIT

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Australia 50 11
India 25 17
Malaysia 12 23

Table 2 .12 A three-dimensional dice from a three-dimensional cube (SSemester 2001-


Country \ Degree BCom BIT

Australia 53 10
India 22 13
Malaysia 19 20

A dice may be shown as in Figure 8.5

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Figure 2.5 A dice from the cube in Figure 8.2

Slice and dice from often figure in interactive use of an OLAP system in which the user
can navigate through the cube by specifying either one or two dimensions or values of all
three dimensions that are of interest.

Pivot or Rotate
The pivot operation is used when the user wishes to re-orient the view of the data cube. It
may involve swapping the rows and columns, or moving one of the row dimensions into
the column dimension. For example, the cube consisting of Tables 8.6, 8.7 and 8.8 gives
the dimension degree along the x-axis, country along the y-axis and starting semester
along the z-axis (or the vertical axis). One may want to swap the dimensions country and

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starting semester. The cube will then consist of several tables like those given in Tables
8.13 and 8.14 on top of each other.

Table 2.13 One table on which other similar tables are piled up in a rotated cube
(Country dimension value = Australia)

Semester \ Degree BSc LLB MBBS BCom BIT ALL

2000-01 5 20 15 50 11 101
2001-01 7 10 16 53 10 96
ALL 12 30 31 103 21 197

Table 2.14 Another table that is part of a rotated cube (Country dimension value = India)

Semester \ Degree BSc LLB MBBS BCom BIT ALL

2000-01 10 0 15 25 17 67
2001-01 9 0 17 22 13 61
ALL 19 0 32 47 30 128

Clearly this rotated cube gives a different view of the same data. Such views can be
particularly useful if the number of dimensions is greater than three.


Following are a number of guidelines for successful implementation of OLAP. The

guidelines are, somewhat similar to those presented for data warehouse implementation.

1. Vision: The OLAP team must, in consultation with the users, develop a clear vision for
the OLAP system. This vision including the business objectives should be clearly
defined, understood, and shared by the stakeholders.

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2. Senior management support: The OLAP project should be fully supported by the
senior managers. Since a data warehouse may have been developed already, this should
not be difficult.
3. Selecting an OLAP tool: The OLAP team should familiarize themselves with the
ROLAP and MOLAP tools available in the market. Since tools are quite different, careful
planning may be required in selecting a tool that is appropriate for the enterprise. In some
situations, a combination of ROLAP and MOLAP may be most effective.
4. Corporate strategy: The OLAP strategy should fit in with the enterprise strategy and
business objectives. A good fit will result in the OLAP tools being used more widely.
5. Focus on the users: The OLAP project should be focused on the users. Users
in consultation with the technical professional, decide what tasks will be done first and
what will be done later. Attempts should be made to provide each user with a tool
suitable for that persons skill level and information needs. A good GUI user interface
should be provided to non-technical users. The project can only be successful with the
full support of the users.
6. Joint management: The OLAP project must be managed by both the IT and business
professionals. Many other people should be involved in supplying ideas. An appropriate
committee structure may be necessary to channel these ideas.
7. Review and adapt: As noted in last chapter, organizations evolve and so must the
OLAP systems. Regular reviews of the project may be required to ensure that the project
is meeting the current needs of the enterprise.


There is much OLAP software available in the market.

A list is available at
Another is available at
The list below provides some major OLAP software.

BI2M (Business Intelligence to Marketing and Management) from B&M Services

has three modules one of which is for OLAP. The OLAP module allows database

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exploring including slice and dice, roll-up, drill-down and displays results as 2D
charts, 3D charts and tables.

Business Objects OLAP Intelligence from BusinessObjects allows access to

OLAP servers from Microsoft, Hyperion, IBM and SAP. Usual operations like
slice and dice, and drill directly on multidimensional sources are possible.
BusineeOjects also has widely used Crystal Analysis and Reports.

ContourCube from Contour Components is an OLAP product that enables users

to slice and dice, roll-up, drill-down and pivot efficiently.

DB2 Cube Views from IBM includes features and functions for managing and
deploying multidimensional data. It is claimed that OLAP solutions can be
deployed quickly.

Essbase Integration Services from Hyperion Solutions is a widely used suite of

tools. The companys Web sites make it difficult to understand what the software
does. It 2005 market ranking was 2.
Everest from OutlookSoft is a Microsoft-based single application and database
that provides operational reporting and analysis including OLAP and
multidimensional slice and dice and other analysis operations.

Executive Suite from CIP-Global is an integrated corporate planning, forecasting,

consolidation and reporting solution based on Microsofts SQL server 2000 and
analysis Services Platform.

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Executive Viewer from Temtec provides users real-time Web access to OLAP
databases such as Microsoft Analysis Services and Hyperion Essbase for
advanced and ad hoc analysis as well as reporting.

Express and the Oracle OLAP Option Express is a multidimensional database

and application development environment for building OLAP applications. It is
MOLAP. OLAP Analytic workspaces is a porting of the Oracle Express analytic
engine to the Oracle RDBMS kernel which now runs as an OLAP virtual

MicroStrategy 8 from MicroStrategy provides facilities for query, reporting and

advanced analytical needs. It 2005 market ranking was 5.

NovaView from Panorama extends the Microsoft platform that integrates

analysis, reporting and performance measurement information into a single

PowerPlay from Cognos is widely used OLAP software that allows users to
analyze large volumes of data with fast response times. Its 2005 market ranking
was 3.
SQL Server 2000 Analysis Services from Microsoft. SQL Server 2000 Analysis
Services is the OLAP Services component in SQL Server 7.0

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Unit 3

The complexity of modern society coupled with growing competition due to trade
globalization has fuelled the demand for data mining. Most enterprises have collected
information over at least the last 30 years and they are keen to discover business
intelligence that might be buried in it. Business intelligence may be in the form of
customer profiles which may result in better targeted marketing and other business
During the 1970s and the 1980s, most Western societies developed a similar set of
privacy principles and most of them enacted legislation to ensure that governments and
the private sector were following good privacy principles. Data mining is a relatively new
technology and privacy principles developed some 20-30 years ago are not particularly
effective in dealing with privacy concerns that are being raised about data mining. These
concerns have been heightened by the dramatically increased use of data mining by
governments as a result of the 9/11 terrorist attacks. A number of groups all over the
world are trying to wrestle with the issues raised by widespread use of data mining

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3.2 Challenges

The daily use of the word privacy about information sharing and analysis is often vague
and may be misleading. We will therefore provide a definition (or two). Discussions
about the concept of information privacy started in the 1960s when a number of
researchers recognized the dangers of privacy violations by large collections of personal
information in computer systems. Over the years a number of definitions of information
privacy have emerged. One of them defines information privacy as the individuals
ability to control the circulation of information relating to him/her. Another widely used
definition is the claim of individuals, groups, or institutions to determine for themselves
Sometimes privacy is confused with confidentially and at other times with
security. Privacy does involve confidentiality and security but it involves more than the


During the 1970s and 1980s many countries and organizations (e.g. OECD, 1980)
developed similar basic information privacy principles which were then enshrined in
legislation by many nations. These principles are interrelated and party overlapping and
should therefore be treated together. The OECD principles are:
1. Collection limitation: There should be limits to the collection of personal data
appropriate, with the knowledge or consent of the data subject.
2. Data quality: Personal data should be relevant to the purposes for which they are
complete and kept up-to-data.
3. Purpose specification: The purpose for which personal data are collected should

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4. Use limitation: Personal data should not be disclosed, made available or

otherwise used for purposes other than those specified in accordance with
Principle 3 except with the consent of the data subject or by the authority of law.
5. Security safeguards: Personal data should be protected by reasonable security
safeguards against such risks as loss of unauthorized access, destruction, use,
modification or disclosure of data.
6. Openness: There should be general policy of openness about developments,
available for establishing the existence and nature of personal data, and the main
7. Individual participation: An individual should have the right:
(a) to obtain from a data controller, or otherwise, confirmation of whether or
not the data controller has data relating to him;
(b) to have communicated to him, data relating to him
within a reasonable time;
at a charge, if any, that is not excessive;
in a reasonable manner; and
in a form that is readily intelligible to him;
denied, and to be able to challenge such denial; and
(d) to challenge data related to him and, if the challenge is successful, to have
the data erased, rectified, completed or amended.
8. Accountability: A data controller should be accountable for complying with
measures which give effect to the principles stated above.

These privacy protection principles were developed for online transaction processing
(OLTP) systems before technologies like data mining became available. In OLTP
systems, the purpose of the system is quite clearly defined since the system is used for a
particular operational purpose of an enterprise (e.g. student enrolment). Given a clear
purpose of the system, it is then possible to adhere to the above principles.

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Data mining involves the extraction of implicit, previously unknown and potentially
useful knowledge from large databases. Data mining is a very challenging task since it
involves building and using software that will manage, explore, summarize, model,
analyse and interpret large datasets in order to identify patterns and abnormalities.
Data mining techniques are being used increasingly in a wide variety of
applications. The applications include fraud prevention, detecting tax avoidance, catching
drug smugglers, reducing customer churn and learning more about customers
behaviour. There are also some (mis)uses of data mining that have little to do with
any of these applications. For example, a number of newspapers in early 2005 have
reported results of analyzing associations between the political party that a person votes
for and the car the person drives. A number of car models have been listed in the USA
for each of the two
major political parties.
In the wake of the 9/11 terrorism attacks, considerable use of personal
information, provided by individuals for other purposes as well as information collected
by governments including intercepted emails and telephone conversations, is being made
in the belief that such information processing (including data mining) can assist in
identifying persons who are likely to be involved in terrorist networks or individuals who
might be in contact with such persons or other individuals involved in illegal activities
(e.g. drug smuggling). Under legislation enacted since 9/11, many governments are able
to demand access to most private sector data. This data can include records on travel,
shopping, utilities, credit, telecommunications and so on. Such data can them be mined in
the belief that patterns can be found that will help in identifying terrorists or drug
Consider a very simple artificial example of data in Table 9.1 being analysed
using a data mining technique like the decision tree:

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Table 3.1 A simple data mining example

BirthCountry Age Religion VisitedX StudiedinWest
Risk Class

A <30 P Yes Yes

B >60 Q Yes Yes
A <30 R Yes No
X 30-45 R No No
Y 46-60 S Yes No
X >60 P Yes Yes
Z <25 P No Yes
A <25 Q Yes No
B <25 Q Yes No
B 30-45 S Yes No

Using the decision tree to analyse this data may result in rules like the following:

If Age = 30-45 and Birth Country = A and Visited X = Yes and Studied in West =
Yes and
Religion = R then Risk Class = A.

User profiles are built based on relevant user characteristics. The number of
characteristics may be large and may include all kinds of information including telephone
zones phoned, travelling on the same flight as a person on a watch list and much more.
User profiling is used in a variety of other areas, for example authorship analysis or
plagiarism detection.
Once a user profile is formed, the basic action of the detection system is to
compare incoming personal data to the profile and make a decision as to whether the data

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fit any of the profiles. The comparison can in fact be quite complex because not all of the
large numbers of characteristics in the profile are likely to match but a majority might.
Such profile matching can lead to faulty inferences. As an example, it was
reported that a person was wrongly arrested just because the person had an Arab name
and obtained a driver license at the same motor vehicle office soon after one of the 9/11
hijackers did. Although this incident was not a result of data mining, it does show that an
innocent person can be mistaken for a terrorist or a drug smuggler as a result of some
matching characteristics.


Essentially most data mining techniques that we are concerned about are designed to
discover and match profiles. The aims of the majority of such data mining activities are
laudable but the techniques are not always perfect. What happens if a person matches the
profile but does not belong to the category?
Perhaps it is not a matter of great concern if a telecommunications company
labels a person as one that is likely to switch and then decides to target that person with a
special campaign designed to encourage the person to stay. On the other hand, if the
Customs department identifies a person as fitting the profile of a drug smuggler then that
person is likely to undergo a special search whenever he/she returns home from overseas
and perhaps at other airports if the customs department of one country shares information
with other countries. This would be a matter of much more concern to governments.
Knowledge about the classification or profile of an individual who has been so
classified or profiled may lead to disclosure of personal information with some given
probability. The characteristics that someone may be able to deduce about a person with
some possibility may include sensitive information, for example, race, religion, travel
history, and level of credit card expenditure.
Data mining is used for many purposes that are beneficial to society, as the list of
some of the common aims of data mining below shows.
The primary aim of many data mining applications is to understand the customer
better and improve customer services.

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Some applications aim to discover anomalous patterns in order to help identify,

for example, fraud, abuse, waste, terrorist suspects, or drug smugglers.
In many applications in private enterprises, the primary aim is to improve the
profitability of an enterprise
The primary purpose of data mining is to improve judgement, for example, in
making diagnoses, in resolving crime, in sorting out manufacturing problems, in
predicting share prices or currency movements or commodity prices.
In some government applications, one of the aims of data mining is to identify
criminal and fraud activities.
In some situations, data mining is used to find patterns that are simply not
possible without the help of data mining, given the huge amount of data that must
be processed.

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3.3 Data Mining Tasks

Data mining functionalities are used to specify the kind of patterns to be found in data mining
tasks. In general, data mining tasks can be classified into two categories:

x Predictive tasks. The objective of these tasks is to predict the value of a particular
attribute based on the values of other attribute.

Use some variables (independent/explanatory variable) to predict unknown or

future values of other variables (dependent/target variable).

x Description Methods: Here the objective is to derive patterns that summarize the
underlying relationships in data.

Find human-interpretable patterns that describe the data.

There are four core tasks in Data Mining:

i. Predictive modeling ii. Association analysis

iii. Clustering analysis, iv. Anomaly detection

Descriptive mining tasks characterize the general properties of the data in the database.
Predictive mining tasks perform inference on the current data in order to make predictions.

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Describe data mining functionalities, and the kinds of patterns they can discover (or) define each
of the following data mining functionalities: characterization, discrimination, association and
correlation analysis, classification, prediction, clustering, and evolution analysis. Give examples
of each data mining functionality, using a real-life database that you are familiar with.

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1). predictive method

Find some missing or unavailable data values rather than class labels referred to as prediction. Although
prediction may refer to both data value prediction and class label prediction, it is usually confined to data value
prediction and thus is distinct from classification. Prediction also encompasses the identification of distribution
trends based on the available data.
Predicting flooding is difficult problem. One approach is uses monitors placed at various points in the river. These
monitors collect data relevant to flood prediction: water level, rain amount, time, humidity etc. These water levels at
a potential flooding point in the river can be predicted based on the data collected by the sensors upriver from this
point. The prediction must be made with respect to the time the data were collected

x It predicts categorical class labels

x It classifies data (constructs a model) based on the training set and the values (class labels) in a
classifying attribute and uses it in classifying new data

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x Typical Applications

o credit approval

o target marketing

o medical diagnosis

o treatment effectiveness analysis

Classification can be defined as the process of finding a model (or function) that describes and
of objects whose class label is unknown. The derived model is based on the analysis of a set of training
data (i.e., data objects whose class label is known).

An airport security screening station is used to deter mine if passengers are potential terrorist or criminals. To do
this, the face of each passenger is scanned and its basic pattern(distance between eyes, size, and shape of mouth,
head etc) is identified. This pattern is compared to entries in a database to see if it matches any patterns that are
associated with known offenders

A classification model can be represented in various forms, such as

1) IF-THEN rules,

student ( class , "undergraduate") AND concentration ( level, "high") ==> class A

student (class ,"undergraduate") AND concentrtion (level,"low") ==> class B

student (class , "post graduate") ==> class C

2) Decision tree

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3) Neural network.

Classification vs. Prediction
Classification differs from prediction in that the former is to construct a set of models (or functions) that describe
and distinguish data class or concepts, whereas the latter is to predict some missing or unavailable, and often
numerical, data values. Their similarity is that they are both tools for prediction: Classification is used for predicting
the class label of data objects and prediction is typically used for predicting missing numerical data values.

2). Association Analysis

It is the discovery of association rules showing attribute-value conditions that occur frequently together in a given
set of data. For example, a data mining system may find association rules like
major(X, computing science) owns(X, personal computer)
[support = 12%, confidence = 98%]
where X is a variable representing a student. The rule indicates that of the students under study, 12% (support) major
in computing science and own a personal computer. There is a 98% probability (confidence, or certainty) that a
student in this group owns a personal computer.

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A grocery store retailer to decide whether to but bread on sale. To help determine the impact of this decision, the
retailer generates association rules that show what other products are frequently purchased with bread. He finds 60%
of the times that bread is sold so are pretzels and that 70% of the time jelly is also sold. Based on these facts, he tries
to capitalize on the association between bread, pretzels, and jelly by placing some pretzels and jelly at the end of the
aisle where the bread is placed. In addition, he decides not to place either of these items on sale at the same time.

3). Clustering analysis

Clustering analyzes data objects without consulting a known class label. The objects are clustered or
grouped based on the principle of maximizing the intra-class similarity and minimizing the
interclass similarity. Each cluster that is formed can be viewed as a class of objects.

Example:A certain national department store chain creates special catalogs targeted to various
demographic groups based on attributes such as income, location and physical characteristics of potential
customers (age, height, weight, etc). To determine the target mailings of the various catalogs and to assist
in the creation of new, more specific catalogs, the company performs a clustering of potential customers
based on the determined attribute values. The results of the clustering exercise are the used by
management to create special catalogs and distribute them to the correct target population based on the
cluster for that catalog.

Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes
that group similar events together as shown below:

Classification vs. Clustering

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x In general, in classification you have a set of predefined classes and want to know which class a
new object belongs to.

x Clustering tries to group a set of objects and find whether there is some relationship between the

x In the context of machine learning, classification is supervised learning and clustering is

unsupervised learning.

4). Anomaly Detection

It is the task of identifying observations whose characteristics are significantly different from the
rest of the data. Such observations are called anomalies or outliers. This is useful in fraud
detection and network intrusions.

3.4 Types of Data

A Data set is a Collection of data objects and their attributes. An data object is also known as
record, point, case, sample, entity, or instance. An attribute is a property or characteristic of an
object. Attribute is also known as variable, field, characteristic, or feature.

3.4.1 Attributes and Measurements

An attribute is a property or characteristic of an object. Attribute is also known as variable,

field, characteristic, or feature. Examples: eye color of a person, temperature, etc. A collection of
attributes describe an object.
Attribute Values: Attribute values are numbers or symbols assigned to an attribute. Distinction
between attributes and attribute values Same attribute can be mapped to different attribute
values. Example: height can be measured in feet or meters.
The way you measure an attribute is somewhat may not match the attributes properties.

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Different attributes can be mapped to the same set of values. Example: Attribute values for ID
and age are integers. But properties of attribute values can be different, ID has no limit but age
has a maximum and minimum value.

The types of an attribute

A simple way to specify the type of an attribute is to identify the properties of numbers that
correspond to underlying properties of the attribute.

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x Properties of Attribute Values

The type of an attribute depends on which of the following properties it possesses:
Distinctness: =
Order: < >
Addition: + -
Multiplication: * /
There are different types of attributes
Examples: ID numbers, eye color, zip codes
Examples: rankings (e.g., taste of potato chips on a scale from 1-10), grades, height in {tall,
medium, short}
Examples: calendar dates, temperatures in Celsius or Fahrenheit.
Examples: temperature in Kelvin, length, time, counts

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3.4.2 Describing attributes by the number of values

Discrete Attribute Has only a finite or countably infinite set of values, examples: zip codes,
counts, or the set of words in a collection of documents, often represented as integer variables.
Binary attributes are a special case of discrete attributes
Continuous Attribute Has real numbers as attribute values, examples: temperature, height,
or weight. Practically, real values can only be measured and represented using a finite number of
digits. Continuous attributes are typically represented as floating-point variables.
Asymmetric Attribute-only a non-zero attributes value which is different from other values.

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Preliminary investigation of the data to better understand its specific characteristics, it can help
to answer some of the data mining questions
To help in selecting pre-processing tools
To help in selecting appropriate data mining algorithms
Things to look at: Class balance, Dispersion of data attribute values, Skewness, outliers,
missing values, attributes that vary together, Visualization tools are important, Histograms, box
plots, scatter plots Many datasets have a discrete (binary) attribute class
Data mining algorithms may give poor results due to class imbalance problem, Identify the
problem in an initial phase.

General characteristics of data sets:

x Dimensionality: of a data set is the number of attributes that the objects in the data set
possess. Curse of dimensionality refers to analyzing high dimensional data.
x Sparsity: data sets with asymmetric features like most attributes of an object with value 0;
in some cases it may be with value non-zero.
x Resolution: it is possible to obtain different levels of resolution of the data.
Now there are varieties of data sets are there, let us discuss some of the following.
1. Record
Data Matrix
Document Data
Transaction Data

2. Graph
World Wide Web
Molecular Structures

3. Ordered
Spatial Data
Temporal Data
Sequential Data
- Genetic Sequence Data

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Record Data
Data that consists of a collection of records, each of which consists of a fixed set of attributes

Transaction or market basket Data

A special type of record data, wher each transaction (record) involves a set of items. For
example, consider a grocery store. The set of products purchased by a customer during one
shopping trip constitute a transaction, while the individual products that were purchased are the

Transaction data is a collection of sets of items, but it can be viewed as a set of records whose
fields are asymmetric attributes.
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Transaction data can be represented as sparse data matrix: market basket representation
Each record (line) represents a transaction
Attributes are binary and asymmetric

Data Matrix
An M*N matrix, where there are M rows, one for each object, and N columns, one for each
attribute. This matrix is called a data matrix, which holds only numeric values to its cells.
If data objects have the same fixed set of numeric attributes, then the data objects can be
thought of as points in a multi-dimensional space, where each dimension represents a distinct
Such data set can be represented by an m by n matrix, where there are m rows, one for each
object, and n columns, one for each attribute

The Sparse Data Matrix

It is a special case of a data matrix in which the attributes are of the same type and are
asymmetric; i.e. , only non-zero values are important.
Document Data

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Each document becomes a `term' vector, each term is a component (attribute) of the vector, and
the value of each component is the number of times the corresponding term occurs in the

Graph-based data
In general, the data can take many forms from a single, time-varying real number to a complex
interconnection of entities and relationships. While graphs can represent this entire spectrum of
data, they are typically used when relationships are crucial to the domain. Graph-based data
mining is the extraction of novel and useful knowledge from a graph representation of data.
Graph mining uses the natural structure of the application domain and mines directly over that
structure. The most natural form of knowledge that can be extracted from graphs is also a graph.
Therefore, the knowledge, sometimes referred to as patterns, mined from the data are typically
expressed as graphs, which may be sub-graphs of the graphical data, or more abstract
expressions of the trends reflected in the data. The need of mining structural data to uncover
objects or concepts that relates objects (i.e., sub-graphs that represent associations of features)
has increased in the past ten years, involves the automatic extraction of novel and useful
knowledge from a graph representation of data. a graph-based knowledge discovery system that
finds structural, relational patterns in data representing entities and relationships. This algorithm
was the first proposal in the topic and has been largely extended through the years. It is able to
develop graph shrinking as well as frequent substructure extraction and hierarchical conceptual
one vertices to another and can be represented as a pair of vertices. Typically each edge in a
graph is given a label. Edges can also be associated with a weight.
We denote the vertex set of a graph g by V (g) and the edge set by E(g). A label function, L,
maps a vertex or an edge to a label. A graph g is a sub-graph of another graph g' if there exists a
sub-graph isomorphism from g to g'. (Frequent Graph) Given a labeled graph dataset, D = {G1,
G2, . . . , Gn}, support (g) [or frequency(g)] is the percentage (or number) of graphs in D where g
is a sub-graph. A frequent (sub) graph is a graph whose support is no less than a minimum
support threshold, min support.

Spatial data
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Also known as geospatial data or information it is the data or information that

identifies the geographic location of features and boundaries on Earth, such as natural or
constructed features, oceans, and more. Spatial data is usually stored as coordinates and
topology, and is data that can be mapped. Spatial data is often accessed, manipulated or analyzed
through Geographic Information Systems (GIS).

Measurements in spatial data types: In the planar, or flat-earth, system, measurements of

geometry data type, the distance between (2, 2) and (5, 6) is 5 units, regardless of the units used.

In the ellipsoidal or round-earth system, coordinates are given in degrees of latitude and
longitude. However, lengths and areas are usually measured in meters and square meters, though

Orientation of spatial data: In the planar system, the ring orientation of a polygon is not an
important factor. For example, a polygon described by ((0, 0), (10, 0), (0, 20), (0, 0)) is the same
as a polygon described by ((0, 0), (0, 20), (10, 0), (0, 0)). The OGC Simple Features for SQL

Time Series Data

and time t on the horizontal axis. Time is called the independent variable (in this case however,
something over which you have little control). There are two kinds of time series data:

1. Continuous, where we have an observation at every instant of time, e.g. lie detectors,
electrocardiograms. We denote this using observation X at time t, X(t).

2. Discrete, where we have an observation at (usually regularly) spaced intervals. We

denote this as Xt.

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Economics - weekly share prices, monthly profits
Meteorology - daily rainfall, wind speed, temperature
Sociology - crime figures (number of arrests, etc), employment figures

Sequence Data
Sequences are fundamental to modeling the three primary medium of human communication:
speech, handwriting and language. They are the primary data types in several sensor and
monitoring applications. Mining models for network intrusion detection view data as sequences
of TCP/IP packets. Text information extraction systems model the input text as a sequence of
words and delimiters. Customer data mining applications profile buying habits of customers as a
sequence of items purchased. In computational biology, DNA, RNA and protein data are all best
modeled as sequences.
A sequence is an ordered set of pairs (t1 x1) . . . (tn xn) where ti denotes an ordered attribute like
time (ti1 _ ti) and xi is an element value. The length n of sequences in a database is typically
variable. Often the first attribute is not explicitly specified and the order of the elements is
implicit in the position of the element. Thus, a sequence x can be written as x1 . . . xn. The
elements of a sequence are allowed to be of many different types. When xi is a real number, we
get a time series. Examples of such sequences abound stock prices along time, temperature

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measurements obtained from a monitoring instrument in a plant or day to day carbon monoxide
levels in the atmosphere. When si is of discrete or symbolic type we have a categorical sequence.

3.6 Measures of Similarity and Dissimilarity, Data Mining Applications

Data mining focuses on (1) the detection and correction of data quality problems (2) the use of
algorithms that can tolerate poor data quality. Data are of high quality "if they are fit for their
intended uses in operations, decisionmaking and planning" (J.M.Juran). Alternatively, the data
are deemed of high quality if they correctly represent the real-world construct to which they
refer. Furthermore, apart from these definitions, as data volume increases, the question of
internal consistency within data becomes paramount, regardless of fitness for use for any
external purpose, e.g. a person's age and birth date may conflict within different parts of a
database. The first views can often be in disagreement, even about the same set of data used for
the same purpose.
Definitions are:
x Data quality: The processes and technologies involved in ensuring the conformance of
data values to business requirements and acceptance criteria.

x Data exhibited by the data in relation to the portrayal of the actual scenario.

x The state of completeness, validity, consistency, timeliness and accuracy that makes data
appropriate for a specific use.

Data quality aspects: Data size, complexity, sources, types and formats Data processing issues,
techniques and measures We are drowning in data, but starving of knowledge (Jiawei Han).
Dirty data
What does dirty data mean?
Incomplete data(missing attributes, missing attribute values, only aggregated data, etc.)
Inconsistent data (different coding schemes and formats, impossible values or out-of-range
values), Noisy data (containing errors and typographical variations, outliers, not accurate values)

Data quality is a perception or an assessment of data's fitness to serve its purpose in a given

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Aspects of data quality include:

x Accuracy
x Completeness
x Update status
x Relevance
x Consistency across data sources
x Reliability
x Appropriate presentation
x Accessibility

3.7.1 Measurement and data collection issues

Just think about the statement below a person has a height of 2 meters, but weighs only 2kg`s .
This data is inconsistence. So it is unrealistic to expect that data will be perfect.
Measurement error refers to any problem resulting from the measurement process. The
numerical difference between measured value to the actual value is called as an error. Both of
these errors can be random or systematic.
Noise and artifacts
Noise is the random component of a measurement error. It may involve the distortion of a value
or the addition of spurious objects. Data Mining uses some robust algorithms to produce
acceptable results even when noise is present.
Data errors may be the result of a more deterministic phenomenon called as artifacts.
Precision, Bias, and Accuracy
The quality of measurement process and the resulting data are measured by Precision and Bias.
Accuracy refers to the degree of measurement error in data.
Missing Values
It is not unusual for an object to be missed its attributes. In some cases information is not
collected properly. Example application forms , web page forms.
Strategies for dealing with missing data are as follows:
x Eliminate data objects or attributes with missing values.
x Estimate missing values

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x Ignore the missing values during analysis

Inconsistent values
Suppose consider a city like kengeri which is having zipcode 560060, if the user will give some
other value for this locality then we can say that inconsistent value is present.
Duplicate data
Sometimes Data set contain same object more than once then it is called duplicate data. To detect
and eliminate such a duplicate data two main issues are addressed here; first, if there are two
objects that actually represent a single object, second the values of corresponding attributes may
Issues related to applications are timelines of the data, knowledge about the data and relevance of
the data.

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This chapter presents a methodology known as association analysis, which is useful for
discovering interesting relationships hidden in large data sets. The uncovered relationships can
be represented in the form of association rules or sets of frequent items. For example, the
following rule can be extracted from the data set shown in Table 4.1:

{Diapers} {Beer}.

Table 4.1. An example of market basket transactions.

1 {Bread, Milk}

2 { Bread, Diapers, Beer, Eggs}

3 {Milk, Diapers, Beer, Cola}
4 {Bread, Milk, Diapers, Beer}
5 {Bread, Milk, Diapers, Cola}

The rule suggests that a strong relationship exists between the sale of diapers and beer
because many customers who buy diapers also buy beer. Retailers can use this type of rules to
help them identify new opportunities for cross- selling their products to the customers.

4.1 Basic Concepts and Algorithms

This section reviews the basic terminology used in association analysis and presents a formal
description of the task.
Binary Representation Market basket data can be represented in a binary format as shown in
Table 4.2, where each row corresponds to a transaction and each column corresponds to an item.

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An item can be treated as a binary variable whose value is one if the item is present in a
transaction and zero otherwise. Because the presence of an item in a transaction is often
considered more important than its absence, an item is an asymmetric binary variable.

Table 4.2 A binary 0/1 representation of market basket data.

This representation is perhaps a very simplistic view of real market basket data because it
ignores certain important aspects of the data such as the quantity of items sold or the price paid
to purchase them. Itemset and Support Count Let I = {i1,i2,. . .,id} be the set of all items in a
market basket data and T = {t1, t2, . . . , tN } be the set of all transactions. Each transaction ti
contains a subset of items chosen from I. In association analysis, a collection of zero or more
items is termed an itemset. If an itemset contains k items, it is called a k-itemset. For instance,
{Beer, Diapers, Milk} is an example of a 3-itemset. The null (or empty) set is an itemset that
does not contain any items.
The transaction width is defined as the number of items present in a transaction. A
transaction tj is said to contain an itemset X if X is a subset of tj. For example, the second
transaction shown in Table 6.2 contains the item-set {Bread, Diapers} but not {Bread, Milk}. An
important property of an itemset is its support count, which refers to the number of transactions
that contain a particular itemset. Mathematically, the support count, (X), for an itemset X can
be stated as follows:

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Where the symbol | | denote the number of elements in a set. In the data set shown in Table
4.2, the support count for {Beer, Diapers, Milk} is equal to two because there are only two
transactions that contain all three items.

Association Rule An association rule is an implication expression of the form X Y , where

X and Y are disjoint itemsets, i.e., X Y = 0. The strength of an association rule can be
measured in terms of its support and confidence. Support determines how often a rule is
applicable to a given data set, while confidence determines how frequently items in Y
appear in transactions that contain X . The formal definitions of these metrics are
Support s(X------->Y) = (XUY) 4.1

Confidence C(X------>Y) = (XUY) 4.2

Formulation of Association Rule Mining Problem The association rule mining problem
can be formally stated as follows:
Definition 4.1 (Association Rule Discovery). Given a set of transactions T , find all the rules
having support minsup and confidence minconf, wher minsup and minconf are the
corresponding support and confidence thresholds.
From Equation 4.2, notice that the support of a rule X - Y depends only on the support of
its corresponding itemset, X Y . For example, the following rules have identical support
because they involve items from the same itemset,
{Beer, Diapers, Milk}:
{Beer, Diapers} -{Milk}, {Beer, Milk} -{Diapers},
{Diapers, Milk} -{Beer}, {Beer} -{Diapers, Milk},
{Milk} -{Beer,Diapers}, {Diapers} -{Beer,Milk}.
If the itemset is infrequent, then all six candidate rules can be pruned immediately without
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our having to compute their confidence values. Therefore, a common strategy adopted by many
association rule mining algorithms is to decompose the problem into two major subtasks:
1. Frequent Itemset Generation, whose objective is to find all the item-sets that satisfy the
minsup threshold. These itemsets are called frequent itemsets.
2. Rule Generation, whose objective is to extract all the high-confidence rules from the
frequent itemsets found in the previous step. These rules are called strong rules.
The computational requirements for frequent itemset generation are generally more
expensive than those of rule generation.

Figure 4.1. An itemset lattice.

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4.2 Frequent Itemset Generation

A lattice structure can be used to enumerate the list of all possible itemsets. Figure 4.1 shows
an itemset lattice for I = {a, b, c, d, e}. In general, a data set that contains k items can potentially
generate up to 2k - 1 frequent itemsets, excluding the null set. Because k can be very large in
many practical applications, the search space of itemsets that need to be explored is
exponentially large.
A brute-force approach for finding frequent itemsets is to determine the support count for
every candidate itemset in the lattice structure. To do this, we need to compare each candidate
against every transaction, an operation that is shown in Figure 4.2. If the candidate is contained
in a transaction, its support count will be incremented. For example, the support for
{Bread,Milk} is incremented three times because the itemset is contained in transactions 1, 4,
and 5. Such an approach can be very expensive because it requires O(NMw) comparisons, where
N is the number of transactions, M =2k - 1 is the number of candidate itemsets, and w is the
maximum transaction width.


TID Items
1Bread, Milk M
2Bread, Diapers, Beer, Eggs
3Milk, Diapers, Beer, Coke
4Bread, Milk, Diapers, Beer
5Bread, Milk, Diapers, Coke

Figure 6.2. Counting the support of candidate itemsets.

There are several ways to reduce the computational complexity of frequent itemset
1. Reduce the number of candidate itemsets (M). The Apriori principle, described in the next
section, is an effective way to eliminate some of the candidate itemsets without counting their
support values.
2. Reduce the number of comparisons. Instead of matching each candidate itemset against

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every transaction, we can reduce the number of comparisons by using more advanced data
structures, either to store the candidate itemsets or to compress the data set.
4.2.1 The Apriori Principle
This section describes how the support measure helps to reduce the number of candidate
itemsets explored during frequent itemset generation. The use of support for pruning candidate
itemsets is guided by the following principle.
Theorem 4.1 (Apriori Principle). If an itemset is frequent, then all of its subsets must also be
frequent. To illustrate the idea behind the Apriori principle, consider the itemset lattice shown in
Figure 4.3. Suppose {c, d, e} is a frequent itemset. Clearly, any transaction that contains {c, d, e}
must also contain its subsets, {c, d},{c, e}, {d, e}, {c}, {d}, and {e}. As a result, if {c, d, e} is
frequent, then all subsets of {c, d, e} (i.e., the shaded itemsets in this figure) must also be

Figure 4.3. An illustration of the Apriori principle.

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If {c, d, e} is frequent, then all subsets of this itemset are frequent.

Conversely, if an itemset such as {a, b} is infrequent, then all of its supersets must be
infrequent too. As illustrated in Figure 6.4, the entire subgraph containing the supersets of {a, b}
can be pruned immediately once {a, b} is found to be infrequent. This strategy of trimming the
exponential search space based on the support measure is known as support-based pruning. Such
a pruning strategy is made possible by a key property of the support measure, namely, that the
support for an itemset never exceeds the support for its subsets. This property is also known as
the anti-monotone property of the support measure.
Definition 4.2 (Monotonicity Property). Let I be a set of items, and J =2I be the power set
of I. A measure f is monotone (or upward closed) if

Figure 4.4. An illustration of support-based pruning.

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If {a, b} is infrequent, then all supersets of {a, b} are infrequent, which means that if X is a
subset of Y , then f(X) must not exceed f(Y ). On the other hand, f is anti-monotone (or
downward closed) if which means that if X is a subset of Y , then f(Y ) must not exceed f(X).

Any measure that possesses an anti-monotone property can be incorporated directly into the
mining algorithm to effectively prune the exponential search space of candidate itemsets, as will
be shown in the next section.

4.2.2 Frequent Itemset Generation in the Apriori Algorithm

Apriori is the first association rule mining algorithm that pioneered the use of support-based
pruning to systematically control the exponential growth of candidate itemsets. Figure 4.5
provides a high-level illustration of the frequent itemset generation part of the Apriori algorithm
for the transactions shown in

Figure 4.5. Illustration of frequent itemset generation using the Apriori algorithm.

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Table 4.1. We assume that the support threshold is 60%, which is equivalent to a minimum
support count equal to 3.
Apriori principle ensures that all supersets of the infrequent 1-itemsets must be infrequent.
Because there are only four frequent 1-itemsets, the number of candidate 2-itemsets generated by
the algorithm is = 6. Two of these six candidates, {Beer, Bread} and {Beer, Milk}, are
subsequently found to be infrequent after computing their support values. The remaining four
candidates are frequent, and thus will be used to generate candidate 3-itemsets. Without support-
based pruning, there are = 20 candidate 3-itemsets that can be formed using the six items given
in this example. With the Apriori principle, we only need to keep candidate 3-itemsets whose
subsets are frequent. The only candidate that has this property is
{Bread, Diapers,Milk}.
The effectiveness of the Apriori pruning strategy can be shown by counting the number of
candidate itemsets generated. A brute-force strategy of enumerating all itemsets (up to size 3) as
candidates will produce

candidates. With the Apriori principle, this number decreases to

candidates, which represents a 68% reduction in the number of candidate itemsets even in
this simple example.
The pseudocode for the frequent itemset generation part of the Apriori algorithm is shown in
Algorithm 4.1. Let Ck denote the set of candidate k-itemsets and Fk denote the set of frequent k-
The algorithm initially makes a single pass over the data set to determine the support of
each item. Upon completion of this step, the set of all frequent 1-itemsets, F1, will be known
(steps 1 and 2).
Next, the algorithm will iteratively generate new candidate k-itemsets using the frequent (k
- 1)-itemsets found in the previous iteration (step 5). Candidate generation is implemented using

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a function called apriori-gen, which is described in Section 4.2.3.

To count the support of the candidates, the algorithm needs to make an additional pass over
the data set (steps 610). The subset function is used to determine all the candidate itemsets in
Ck that are contained in each transaction t.
After counting their supports, the algorithm eliminates all candidate itemsets whose support
counts are less than minsup (step 12).
The algorithm terminates when there are no new frequent itemsets generated.

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4.3 Rule Generation

This section describes how to extract association rules efficiently from a given frequent itemset.
Each frequent k-itemset, Y , can produce up to 2k-2 association rules, ignoring rules that have
empty antecedents or consequents( 0Yor Y 0). An association rule can be extracted by
partitioning the itemset Y into two non-empty subsets, X and Y -X, such that X Y - X satisfies
the confidence threshold. Note that all such rules must have already met the support threshold
because they are generated from a frequent itemset.

Example 4 .2. Let X = {1, 2, 3} be a frequent itemset. There are six candidate association rules
that can be generated from X: {1, 2} {3}, {1, 3} {2}, {2, 3}{1}, {1}{2, 3}, {2}{1, 3},
and {3}{1, 2}. As each of their support is identical to the support for X, the rules must satisfy the
support threshold.
Computing the confidence of an association rule does not require additional scans of the
transaction data set. Consider the rule {1, 2} {3}, which is generated from the frequent itemset X
= {1, 2, 3}. The confidence for this rule is ({1, 2, 3})/({1, 2}). Because {1, 2, 3} is frequent, the
anti-monotone property of support ensures that {1, 2} must be frequent, too. Since the support
counts for both itemsets were already found during frequent itemset generation, there is no need to
read the entire data set again.

4.3.1 Confidence-Based Pruning

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4.3.2 Rule Generation in Apriori Algorithm

The Apriori algorithm uses a level-wise approach for generating association rules, where each
level corresponds to the number of items that belong to the rule consequent. Initially, all the high-
confidence rules that have only one item in the rule consequent are extracted. These rules are then
used to generate new candidate rules. For example, if {acd}{b} and {abd}{c} are high-
confidence rules, then the candidate rule {ad} {bc} is generated by merging the consequents of
both rules. Figure 4.15 shows a lattice structure for the association rules generated from the frequent
itemset {a, b, c, d}.

Figure 4.15. Pruning of association rules using the confidence measure.

Suppose the confidence for {bcd} {a} is low. All the rules containing item a in its
consequent, including {cd} {ab}, {bd}{ac}, {bc} {ad}, and {d} {abc} can be discarded.
The only difference is that, in rule generation, we do not have to make additional passes over
the data set to compute the confidence of the candidate rules. Instead, we determine the confidence
of each rule by using the support counts computed during frequent itemset generation.

Algorithm 4 .2 Rule generation of the Apriori algorithm.

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1: for each frequent k-itemset fk , k 2 do

2: H1 = {i | i fk } {1-item consequents of the
3: call ap-genrules(fk , H1 .)
4: end for

Algorithm 4 .3 Procedure ap-genrules(fk , Hm ).

1: k = |fk | {size of frequent itemset.}
2: m = |Hm | {size of rule consequent.}
3: if k > m + 1 then
4: Hm+1 = apriori-gen(Hm ).
5: for each hm+1 Hm+1 do
6: conf = (fk )/(fk - hm+1).
7: if conf minconf then
8: output the rule (fk - hm+1 ) - hm+1.
9: else
10: delete hm+1 from Hm+1 .
11: end if
12: end for
13: call ap-genrules(fk , Hm+1 .)
14: end if

4.4 Compact Representation of frequent Itemsets

In practice, the number of frequent itemsets produced from a transaction data set can be very
large. It is useful to identify a small representative set of itemsets from which all other frequent
itemsets can be derived. Two such representations are presented in this section in the form of
maximal and closed frequent itemsets.

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Figure 4.16. Maximal frequent itemset.

Definition 4.3 (Maximal Frequent Itemset). A maximal frequent itemset is defined as a

frequent itemset for which none of its immediate supersets are frequent.
To illustrate this concept, consider the itemset lattice shown in Figure 4.16. The itemsets in the
lattice are divided into two groups: those that are frequent and those that are infrequent. A frequent
itemset border, which is represented by a dashed line, is also illustrated in the diagram. Every
itemset located above the border is frequent, while those located below the border (the shaded
nodes) are infrequent. Among the itemsets residing near the border, {a, d}, {a, c, e}, and {b, c, d, e}
are considered to be maximal frequent itemsets because their immediate supersets are infrequent.
An itemset such as {a, d} is maximal frequent because all of its immediate supersets, {a, b, d}, {a,
c, d}, and {a, d, e}, are infrequent. In contrast, {a, c} is non-maximal because one of its immediate
supersets, {a, c, e}, is frequent.

For example, the frequent itemsets shown in Figure 4.16 can be divided into two groups:

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Frequent itemsets that begin with item a and that may contain items c, d, or e. This group
includes itemsets such as {a}, {a, c}, {a, d}, {a, e}, and {a, c, e}.
Frequent itemsets that begin with items b, c, d, or e. This group includes itemsets such as
{b}, {b, c}, {c, d},{b, c, d, e}, etc.

4.4.2 Closed Frequent Itemsets

Closed itemsets provide a minimal representation of itemsets without losing their support
information. A formal definition of a closed itemset is presented below.
Definition 4.4 (Closed Itemset). An itemset X is closed if none of its immediate supersets has
exactly the same support count as X. Put another way, X is not closed if at least one of its
immediate supersets has the same support count as X. Examples of closed itemsets are shown in

Figure 4.17 illustrate the support count of each itemset, we have associated each node (itemset)
in the lattice with a list of its corresponding transaction IDs.

Figure 4.17. An example of the closed frequent itemsets

Definition 4.5 (Closed Frequent Itemset). An itemset is a closed frequent itemset if it is closed

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and its support is greater than or equal to minsup. Algorithms are available to explicitly extract
closed frequent itemsets from a given data set. Interested readers may refer to the bibliographic
notes at the end of this chapter for further discussions of these algorithms. We can use the closed
frequent itemsets to determine the support counts for the non-closed Representation of Frequent

Algorithm 4 .4 Support counting using closed frequent itemsets.

1: Let C denote the set of closed frequent itemsets
2: Let kmax denote the maximum size of closed frequent itemsets
3m:axFk = {f |f C, |f | = kmax } {Find all frequent itemsets of size kmax .}
4: for k = kmax - 1 downto 1 do
5: Fk = {f |f Fk+1 , |f | = k} {Find all frequent itemsets of size k.}
6: for each f Fk do
7: if f C t/hen
8: = max{f .support|f Fk+1 , f f }
9: end if
10: end for
11: end for

The algorithm proceeds in a specific-to-general fashion, i.e., from the largest to the smallest
frequent itemsets. This is because, in order to find the support for a non-closed frequent itemset, the
support for all of its supersets must be known.

Figure 4. 18. Re lationsh ips among frequent, maxi mal frequent, and c losed
frequent itemse ts .

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Figure 4.17 relationship among frequent, maximum frequent, and closed frequent itemset.

Closed frequent itemsets are useful for removing some of the redundant association rules. An
association rule X Y is redundant if there exists another rule X Y , where X is a subset of X
and Y is a subset of Y , such that the support and confidence for both rules are identical. In the
example shown in Figure 4.17, {b} is not a closed frequent itemset while {b, c} is closed.
The association rule {b} {d, e} is therefore redundant because it has the same support and
confidence as {b, c} {d, e}. Such redundant rules are not generated if closed frequent itemsets are
used for rule generation.
4.5 Alternative Methods for Generating Frequent Itemsets
Apriori is one of the earliest algorithms to have successfully addressed the combinatorial
explosion of frequent itemset generation. It achieves this by applying the Apriori principle to prune
the exponential search space. Despite its significant performance improvement, the algorithm still
incurs considerable I/O overhead since it requires making several passes over the transaction data

General-to-Specific versus Specific-to-General: The Apriori algorithm uses a general-to-

specific search strategy, where pairs of frequent (k-1)-itemsets are merged to obtain candidate k-
itemsets. This general-to-specific search strategy is effective, provided the maximum length of a
frequent itemset is not too long. The configuration of frequent item-sets that works best with this
strategy is shown in Figure 4.19(a), where the darker nodes represent infrequent itemsets.

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Alternatively, a specific-to-general search strategy looks for more specific frequent itemsets first,
before finding the more general frequent itemsets. This strategy is use-ful to discover maximal
frequent itemsets in dense transactions, where the frequent itemset border is located near the bottom
of the lattice, as shown in Figure 4.19(b). The Apriori principle can be applied to prune all subsets
of maximal frequent itemsets. Specifically, if a candidate k-itemset is maximal frequent, we do not
have to examine any of its subsets of size k - 1. However, if the candidate k-itemset is infrequent,
we need to check all of its k - 1 subsets in the next iteration. Another approach is to combine both
general-to-specific and specific-to-general search strategies. This bidirectional approach requires
more space to

Figure 6.19. General-to-specific, specific-to-general, and bidirectional search.

Equivalence Classes: Another way to envision the traversal is to first partition the lattice into
disjoint groups of nodes (or equivalence classes). A frequent itemset generation algorithm searches
for frequent itemsets within a particular equivalence class first before moving to another
equivalence class. As an example, the level-wise strategy used in the Apriori algorithm can be
considered to be partitioning the lattice on the basis of itemset sizes;
Breadth-First versus Depth-First: The Apriori algorithm traverses the lattice in a breadth-
first manner, as shown in Figure 6.21(a). It first discovers all the frequent 1-itemsets, followed by
the frequent 2-itemsets, and so on, until no new frequent itemsets are generated.

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Figure 6.20. Equivalence classes based on prefix and suffix labels of item sets

Figure 6.21. Breadth first and depth first traversal

Representation of Transaction Data Set There are many ways to represent a transaction
data set. The choice of representation can affect the I/O costs incurred when computing the support
of candidate itemsets. Figure 6.23 shows two different ways of representing market basket
transactions. The representation on the left is called a horizontal data layout, which is adopted by
many association rule mining algorithms, including Apriori. Another possibility is to store the list of
transaction identifiers (TID-list) associated with each item. Such a representation is known as the

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vertical data layout. The support for each candidate itemset is obtained by intersecting the TID-lists
of its subset items. The length of the TID-lists shrinks as we progress to larger sized itemsets.
Data Layout Vertical Data Layout
TID Items a b c d e
1 a,b,e 1 1 2 2 1
2 b,c,d 4 2 3 4 3
3 c,e 5 5 4 5 6
4 a,c,d 6 7 8 9
5 a,b,c,d 7 8 9
6 a,e
8 10
7 a,b
8 a,b,c
9 a,c,d
10 b

Figure 6.23. Horizontal and vertical data format.

However, one problem with this approach is that the initial set of TID-lists may be too large to
fit into main memory, thus requiring more sophisticated techniques to compress the TID-lists. We
describe another effective approach to represent the data in the next section.

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5.1 Basics
The input data for a classication task is a collection of records. Each record, also known as
an instance or example, is characterized by a tuple (x, y), where x is the attribute set and y is a
special attribute, designated as the class label. sample data set used for classifying vertebrates into
one of the following categories: mammal, bird, sh, reptile, or amphibian. The attribute set
includes properties of a vertebrate such as its body temperature, skin cover, method of
reproduction ability to y, and ability to live in water. the attribute set can also contain continuous
features. The class label, on the other hand, must be a discrete attribute. This is a key
characteristic that distinguishes classication from regression, a predictive modelling task in which
y is a continuous attribute. .

Denition 3.1 (Classication). Classication is the task of learning a target function f that maps
each attribute set x to one of the predened class labels y. The target function is also known
informally as a classication model.A classication model is useful for the following purposes.
Descriptive Modeling
A classication model can serve as an explanatory tool to distinguish between objects of
di erent For example, it would be useful for both biologists and others to have a
descriptive model.

Predictive Modeling
A classication model can also be used to predict the class label of unknown records.
As a classication model can be treated as a black box that automatically a class label
when presented with the attribute set of an unknown record. Suppose we are given the following
characteristics of a creature known as a gila monster: Classication techniques are most suited for
predicting or describing data sets with binary or nominal categories. They are less e ective
for ordinal categories (e.g., to classify a person as a member of high-, medium-, or low-income
group) because they do not consider the implicit order among the categories. Other forms of
relationships, such as the subclasssuper class relationships among categories
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5.2 General Approach to Solving a Classication Problem

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A classication technique (or classier) is a systematic approach to building classication

models from an input data set. Examples include decision tree classiers, rule-based classiers,
neural networks, support vector machines and nave Bayes classiers. Each technique employs a
learning algorithm to identify a model that best ts the relationship between the attribute set and
class label of the input data. The model generated by a learning algorithm should both t the
input data well and correctly predict the class labels of records it has never before.
Therefore, a key objective of the algorithm is to build models with good generalization
capability; i.e., models that accurately predict the class labels of previously unknown records.

Figure 3.3. General approach for building a classi cation model.

Figure 3.3 shows a general approach for solving classication problems. First, a training set
consisting of records whose class labels are known must be The training set is used to
build a classication model, which is subsequently applied to the test set, which consists of records
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with unknown class labels.

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Table 3.2. Confusion matrix for a 2-class problem

Evaluation of the performance of a classication model is based on the counts of test
records correctly and incorrectly predicted by the model. These counts are tabulated in a table
known as a confusion matrix. Table 4.2 depicts the confusion matrix for a binary classication
problem. Each entry fij in this table denotes the number of records from class i predicted to
be of class j. For instance, f01is the number of records from class 0 incorrectly predicted as class 1.
Based on the entries in the confusion matrix, the total number of correct predictions made by the
model is (f11 + f00) and the total number of incorrect predictions is (f10+ f01). Although a confusion
matrix provides the information needed to determine how well a classication model performs,
summarizing this information with a single number would make it more convenient to compare
the performance of di eren models. This can be done using a performance metric such as
accuracy, which is dened as follows:


Equivalently, the performance of a model can be expressed in terms of its error rate, which is
given by the following equation:

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Most classication algorithms seek models that attain the highest accuracy, or equivalently, the
lowest error rate when applied to the test set.

5.3 Decision Tree Induction

This section introduces a decision tree classier, which is a simple yet widely used classication
5.3.1 How a Decision Tree Works
To illustrate how classication with a decision tree works, consider a simpler version of the
vertebrate classication problem described in the previous section. Instead of classifying the
vertebrates into ve distinct groups of species, we assign them to two categories: mammals and
non-mammals. Suppose a new species is discovered by scientists. How can we tell whether it is a
mammal or a non-mammal? One approach is to pose a series of questions about the characteristics
of the species. The rst question we may ask is whether the species is cold- or warm-blooded.
If it is cold-blooded, then it is denitely not a Otherwise, it is either a bird or a mammal.
mammal. In the latter case, we need to ask a follow-up Do the females of the species give birth to
their young? Those that do give birth are denitely mammals, while those that do not are likely to
be non-mammals (with the exception of egg-laying mammals such as the platypus and spiny
anteater) The previous example illustrates how we can solve a classication problem by asking a
series of carefully crafted questions about the attributes of the test record. Each time we
receive an answer, a follow-up question is asked until we reach a conclusion about the class
label of the record. The series of questions and their possible answers can be organized in the form
of a decision tree, which is a hierarchical structure consisting of nodes and directed edges. Figure
4.4 shows the decision tree for the mammal classication problem. The tree has three types of
A root node that has no incoming edges and zero or more outgoing edges.
Internal nodes, each of which has exactly one incoming edge and two or more outgoing edges.
Leaf or terminal nodes, each of which has exactly one incoming edge and no outgoing edges.
In a decision tree, each leaf node is assigned a class label. The non terminal nodes,
which include the root and other internal nodes, contain attribute test conditions to separate
records that have di eren t characteristics.
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For example,
the root node
shown in Figure
4.4 uses

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the attribute Body.

Figure 5.4. A decision tree for the mammal classi cation problem.
Temperature to separate warm-blooded from cold-blooded vertebrates. Since all cold-
blooded vertebrates are non-mammals, a leaf node labeled Non-mammals is created as the right
child of the root node. If the vertebrate is warm-blooded, a subsequent attribute, Gives Birth, is
used to distinguish mammals from other warm-blooded creatures, which are mostly birds.
Classifying a test record is straightforward once a decision tree has been constructed. Starting from
the root node, we apply the test condition to the record and follow the appropriate branch based on
the outcome of the test.

This will lead us either to another internal node, for which a new test condition is applied, or
to a leaf node. The class label associated with the leaf node is then assigned to the record. As an
illustration, Figure 4.5 traces the path in the decision tree that is used to predict the class label of a
amingo. The path terminates at a leaf node labeled Non-mammals.

5.3.2 How to Build a Decision Tree

There are exponentially many decision trees that can be constructed from a given set of
attributes. While some of the trees are more accurate than others, nding the optimal tree is
computationally infeasible because of the exponential size of the search space. Nevertheless,
e cient algorithms have been developed to induce a reasonably accurate, albeit suboptimal,

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decision tree in a reasonable amount of time.

These algorithms usually employ a greedy strategy that grows a decision tree by making a
series of locally optimum decisions about which attribute to use for partitioning the data. One such
algorithm is Hunts algorithm, which is the basis of many existing decision tree induction
algorithms, including ID3, C4.5, and CART.

This section presents a high-level discussion of Hunts algorithm and illustrates some of its
design issues.

Figure 5.5. Classifying an unlabeled vertebrate.

The dashed lines represent the outcomes of applying various attribute test conditions on the
unlabeled vertebrate. The vertebrate is eventually assigned to the Non-mammal class.

Hunts Algorithm
In Hunts algorithm, a decision tree is grown in a recursive fashion by partitioning the
training records into successively purer subsets. Let Dtbe the set of training records that are
associated with node t and y = {y1, y2, . . . , yc} be the class labels. The following is a recursive

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denition of Hunts algorithm.

Step 1: If all the records in Data belong to the same class yt, then t is a leaf node labeled as yt.

Step 2: If Data contains records that belong to more than one class, an attribute test
condition is selected to partition the records into smaller subsets. A child node is created for
each outcome of the test condition and the records in Dt are distributed to the children based on
the outcomes. The algorithm is then recursively applied to each child node.

Figure 3.6. Training set for predicting borrowers who will default on loan payments.

To illustrate how the algorithm works, consider the problem of predicting whether a loan
applicant will repay her loan obligations or become delinquent, subsequently defaulting on her loan.
A training set for this problem can be constructed by examining the records of previous borrowers.
In the example shown in Figure 4.6, each record contains the personal information of a
along with a class label indicating whether the borrower has defaulted on loan payments.
The initial tree for the classication problem contains a single node with class label Defaulted = No (see
Figure 3.7(a)), which means that most of the borrowers successfully repaid their loans. The tree, however, needs to
be redened since the root node contains records from both classes. The records are
Subsequently divided into smaller subsets based on the outcomes of the Home Owner test condition, as shown in Figure
3.7(b). The justication for choosing this attribute test condition will be discussed later. For now, we will assume that
this is the best criterion for splitting the data at this point. Hunts algorithm is then applied recursively to
each child of the root node. From the training set given in Figure 3.6, notice that all borrowers who are home
owners successfully repaid their loans. The left child of the root is therefore a leaf node labelled Defaulted = No (see
Figure 3.7(b)). For the right child, we need to continue applying the recursive step of Hunts algorithm until all the
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records belong to the same class. The trees resulting from each recursive step are shown in Figures 3.7(c) and (d).

Figure 5 .7 Hunt s algorithm for inducing decision trees.

Hunts algorithm will work if every combination of attribute values is present in the
training data and each combination has a unique class label. These assumptions are too
stringent for use in most practical situations. Additional conditions are needed to handle the
following cases:

1. It is possible for some of the child nodes created in Step 2 to be empty; i.e., there are no
records associated with these nodes. This can happen if none of the training records have the
combination of attribute values associated with such nodes. In this case the node is declared a
leaf node with the same class label as the majority class of training records associated with its
parent node.

2. In Step 2, if all the records associated with Dt have identical attribute values (except for
the class label), then it is not possible to split these records any further. In this case, the node is
declared a leaf node with the same class label as the majority class of training records associated
with this node.
Design Issues of Decision Tree Induction

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A learning algorithm for inducing decision trees must address the following two issues.

a) How should the training records be split? Each recursive step of the tree-growing
process must select an attribute test condition to divide the records into smaller
subsets. To implement this step, the algorithm must provide a method for specifying
the test condition for attribute types as well as an objective measure for
di erent
evaluating the goodness of each test condition.
b) How should the splitting procedure stop? A stopping condition is needed to terminate the
tree-growing process. A possible strategy is to continue expanding a node until either all the
records belong to the same class or all the records have identical attribute values. Although
both conditions are su cient to stop any decision tree induction algorithm, other
criteria can be imposed to allow the tree-growing procedure to terminate earlier.

5.3.3 Methods for Expressing Attribute Test Conditions

Decision tree induction algorithms must provide a method for expressing an attribute test condition and its
corresponding outcomes for di erent attribute types. Binary The test condition for a binary attribute
Attributes generates two potential outcomes, as shown in Figure 3.8.


Cold blooded warm blooded

Figure 3.8 Test condition for binary attributes.

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Figure 3.9 Test conditions for nominal attributes.

Nominal Attributes:
Since a nominal attribute can have many values, its test condition can be expressed in
two ways, as shown in Figure 3.9. For a multiway split (Figure 3.9(a)), the number of
outcomes depends on the number of distinct values for the corresponding attribute. For
example, if an attribute such as marital status has three distinct valuessingle, married, or divorced
its test condition will produce a three-way split. On the other hand, some decision tree algorithms,
such as CART, produce only binary splits by considering all 2 1 1 ways of creating a binary
partition of k attribute values. Figure 3.9(b) illustrates three di erent ways of grouping the attribute
values for marital status into two subsets.

Ordinal Attributes:
Ordinal attributes can also produce binary or multiway splits. Ordinal attribute values can
be grouped as long as the grouping does not violate the order property of the attribute values.
Figure 3.10 illustrates various ways of splitting training records based on the Shirt Size attribute.
The groupings shown in Figures 3.10(a) and (b) preserve the order among the attribute
values, whereas the grouping shown in Figure 3.10(c) violates this property because it combines
the attribute values Small and Large into the same partition while Medium and Extra Large are
combined into another partition.

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Figure 3.10 Different ways of grouping ordinal attribute values.

Continuous Attributes:
For continuous attributes, the test condition can be expressed as a comparison test (A < v) or (A
v) with binary outcomes, or a range query with outcomes of the form vi A < vi+1, for i = 1. . . k.
The di erence between these approaches is shown in Figure For the binary case, the decision
tree algorithm must consider all possible split positions v, and it selects the one that produces
the best partition. For the multiway split, the algorithm must consider all possible ranges of
continuous values. One approach is to apply the discretization strategies described. After
discretization, a new ordinal value will be assigned to ach discretized interval. Adjacent intervals
can also be aggregated into wider ranges as long as the order property is preserved.

(a) (b)
Figure 3.11 Test condition for continuous attributes.

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Figure 3.12 Multiway versus binary splits.

5.3.4 Measures for Selecting the Best Split

There are many measures that can be used to determine the best way to split the records.
These measures are dened in terms of the class distribution of the records before and after splitting.

Let p(i|t) denote the fraction of records belonging to class i at a given node t. We sometimes
omit the reference to node t and express the fraction as pi. In a two-class problem, the class
distribution at any node can be written as (p0, p1), where p1 = 1 p0. The class distribution
before splitting is (0.5, 0.5) because there are an equal number of records from each class.
If we split the data using the Gender attribute, then the class distributions of the child nodes are
(0.6, 0.4) and (0.4, 0.6), respectively. Although the classes are no longer evenly distributed, the
child nodes still contain records from both classes. Splitting on the second attribute, Car Type will
result in purer partitions.

The measures developed for selecting the best split are often based on the degree of impurity
of the child nodes. The smaller the degree of impurity, the more skewed the class distribution. For
example, a node with class distribution (0, 1) has zero impurity, whereas a node with uniform class
distribution (0.5, 0.5) has the highest impurity. Examples of impurity measures include

Entropy (t) = p(i|t) log2p(i|t), (3.3)
Gini (t) = 1 [p(i|t)] , (3.4)
Classication error (t) = 1 max [p(i|t)],

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Where c is the number of classes and 0 log20 = 0 in entropy calculations.

Figure 3.13 Comparison among the impurity measures for binary classi cation problems.
Figure 3.13 compares the values of the impurity measures for binary classication problems.
p refers to the fraction of records that belong to one of the two classes. Observe that all three
measures attain their maximum value when the class distribution is uniform (i.e., when p = 0.5).
The minimum values for the measures are attained when all the records belong to the same class
(i.e., when p equals 0 or 1). We next provide several examples of computing the different impurity

Gain Ratio
Impurity measures such as entropy and Gini index tend to favor attributes that have a large
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number of distinct values. Comparing the rst test condition, Gender, with the second, Car
Type, it is easy to see that Car Type seems to provide a better way of splitting the data since
it produces purer descendent nodes. However, if we compare both conditions with Customer ID,
the latter appears to produce purer partitions.
Yet Customer ID is not a predictive attribute because its value is unique for each record.
Even in a less extreme situation, a test condition that results in a
large number of outcomes may not be desirable because the number of record associated
with each partition is too small to enable us to make any reliable predictions.
There are two strategies for overcoming this problem. The rst strategy is to restrict the test
conditions to binary splits only. This strategy is employed by decision tree algorithms such as
CART. Another strategy is to modify the splitting criterion to take into account the number of
outcomes produced by the attribute test condition. For example, in the C4.5 decision tree
algorithm,a splitting criterion known as gain ratio is used to determine the goodness of a split.
This criterion is dened as follows:


Split info
Here, Split Info = ki=1 P (vi) log2P (vi) and k is the total number of splits. For example, if each
attribute value has the same number of records, then i: P (vi) = 1/k and the split
information would be equal to log2k. This example suggests that if an attribute produces a
large number of splits, its split information will also be large, which in turn reduces its gain ratio.
5.3.5 Algorithm for Decision Tree Induction
A skeleton decision tree induction algorithm called Tree Growth is shown in Algorithm 4.1. The input to this
algorithm consists of the training records E and the attribute set F. The algorithm works by recursively selecting the
best attribute to split the data (Step 7) and expanding the leaf nodes of the tree.

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(Steps 11 and 12) until the stopping criterion is met (Step 1). The details of this algorithm are
explained below:

1. The createNode() function extends the decision tree by creating a new node. A node in the
decision tree has either a test condition, denoted as node. Test cond, or a class label, denoted as
node. Label.

2. The find best split () function determines which attribute should be selected as the test condition
for splitting the training records. As previously noted, the choice of test condition depends on
which impurity measure is used to determine the goodness of a split. Some widely used measures
include entropy, the Gini index, and the statistic.

3. The Classify() function determines the class label to be assigned to a leaf node. For each leaf
node t, let p(i|t) denote the fraction of training records from class i associated with the node t. In
most cases, the leaf node is assigned to the class that has the majority number of training records:

leaf.label = argmax p(i|t),i

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where the argmax operator returns the argument i that maximizes the expression p(i|t). Besides
providing the information needed to determine the class label of a leaf node, the fraction p(i|t) can also be used to
estimate the probability that a record assigned to the leaf node t belongs to class i.

4. The stopping cond() function is used to terminate the tree-growing process by testing whether all
the records have either the same class label or the same attribute values. Another way to terminate the recursive
function is to test whether the number of records has fallen below some
Minimum threshold.

After building the decision tree, a tree-pruning step can be performed to reduce the size of the
decision tree. Decision trees that are too large are susceptible to a phenomenon known as overtting. Pruning helps
by trimming the branches of the initial tree in a way that improves the generalization capability of the decision tree.

5.3.6 Characteristics of Decision Tree Induction

The following is a summary of the important characteristics of decision tree
induction algorithms.
1. Decision tree induction is a nonparametric approach for building classification models. In other
words, it does not require any prior assumptions regarding the type of probability distributions
satisfied by the class and other attributes.
2. Finding an optimal decision tree is an NP-complete problem. Many decision tree algorithms
employ a heuristic-based approach to guide their search in the vast hypothesis space. For example,
the algorithm presented in Section 3.3.5 uses a greedy, top-down, recursive partitioning strategy
for growing a decision tree.
3. Techniques developed for constructing decision trees are computationally inexpensive, making
it possible to quickly construct models even when the training set size is very large. Furthermore,
once a decision tree has been built, classifying a test record is extremely fast, with a worst-case
complexity of O(w), where w is the maximum depth of the tree.
4. Decision trees, especially smaller-sized trees, are relatively easy to interpret. The accuracies of
the trees are also comparable to other classification techniques for many simple data sets.
5. Decision trees provide an expressive representation for learning discrete valued functions.
However, they do not generalize well to certain types of Boolean problems. One notable example

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is the parity function, whose value is 0 (1) when there is an odd (even) number of Boolean
attributes with the value True. Accurate modeling of such a function requires a full decision tree
with 2d nodes, where d is the number of Boolean attributes
6. Decision tree algorithms are quite robust to the presence of noise, especially when methods for
avoiding overfitting, are employed.
7. The presence of redundant attributes does not adversely affect the accuracy of decision trees. An
attribute is redundant if it is strongly correlated with another attribute in the data. One of the two
redundant attributes will not be used for splitting once the other attribute has been chosen.
However, if the data set contains many irrelevant attributes, i.e., attributes that are not useful for
the classification task, then some of the irrelevant attributes may be accidently chosen during the
tree-growing process, which results in a decision tree that is larger than necessary.

8. Since most decision tree algorithms employ a top-down, recursive partitioning approach, the
number of records becomes smaller as we traverse down the tree. At the leaf nodes, the number of
records may be too small to make a statistically significant decision about the class representation
of the nodes. This is known as the data fragmentation problem. One possible solution is to disallow
further splitting when the number of records falls below a certain threshold.
9. A subtree can be replicated multiple times in a decision tree, This makes the decision tree more
complex than necessary and perhaps more difficult to interpret. Such a situation can arise from
decision tree implementations that rely on a single attribute test condition at each internal node.
Since most of the decision tree algorithms use a divide-and-conquer partitioning strategy, the same
test condition can be applied to different parts of the attribute space, thus leading to the subtree
replication problem.
10. The test conditions described so far in this chapter involve using only a single attribute at a
time. As a consequence, the tree-growing procedure can be viewed as the process of partitioning
the attribute space into disjoint regions until each region contains records of the same class. The
border between two neighboring regions of different classes is known as a decision boundary.
Constructive induction provides another way to partition the data into homogeneous,
nonrectangular regions

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5.4 Rule-Based Classification

In this section, we look at rule-based classifiers, where the learned model is represented as a set of IF-
THEN rules. We first examine how such rules are used for classification. We then study ways in which they can be
generated, either froma decision tree or directly from the training data using a sequential covering algorithm.
5.4.1 Using IF-THEN Rules for Classification
Rules are a good way of representing information or bits of knowledge. A rule-based classifier uses a set of IF-
THEN rules for classification. An IF-THEN rule is an expression of the form
IF condition THEN conclusion.
An example is rule R1,
R1: IF age = youth AND student = yes THEN buys computer = yes.

The IF-part (or left-hand side) of a rule is known as the rule antecedent or precondition. The THEN-part (or
right-hand side) is the rule consequent. In the rule antecedent, the condition consists of one or more attribute tests
(such as age = youth, and student = yes) that are logically ANDed. The rules consequent contains a class prediction
(in this case, we are predicting whether a customer will buy a computer). R1 can also be written as
R1: (age = youth) ^ (student = yes))(buys computer = yes).
If the condition (that is, all of the attribute tests) in a rule antecedent holds true for a given tuple, we say that the rule
antecedent is satisfied (or simply, that the rule is satisfied) and that the rule covers the tuple.
A rule R can be assessed by its coverage and accuracy. Given a tuple, X, from a class labeled data set,D, let ncovers
be the number of tuples covered by R; ncorrect be the number of tuples correctly classified by R; and jDj be the
number of tuples in D. We can define the coverage and accuracy of R as

That is, a rules coverage is the percentage of tuples that are covered by the rule (i.e., whose attribute values hold
true for the rules antecedent). For a rules accuracy, we look at the tuples that it covers and see what percentage of
them the rule can correctly classify.
5.4.2 Rule Extraction from a Decision Tree

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Decision tree classifiers are a popular method of classificationit is easy to understand how decision trees work and
they are known for their accuracy. Decision trees can become large and difficult to interpret. In this subsection, we
look at how to build a rule based classifier by extracting IF-THEN rules from a decision tree. In comparison with a
decision tree, the IF-THEN rules may be easier for humans to understand, particularly if the decision tree is very
To extract rules from a decision tree, one rule is created for each path from the root to a leaf node.
Each splitting criterion along a given path is logically ANDed to form the rule antecedent (IF part). The leaf node
holds the class prediction, forming the rule consequent (THEN part).

Example 3.4 Extracting classification rules from a decision tree. The decision tree of Figure 6.2 can
be converted to classification IF-THEN rules by tracing the path from the root node to
each leaf node in the tree.

A disjunction (logical OR) is implied between each of the extracted rules. Because the rules are extracted directly
from the tree, they are mutually exclusive and exhaustive. By mutually exclusive, this means that we cannot have
rule conflicts here because no two rules will be triggered for the same tuple. (We have one rule per leaf, and any
tuple can map to only one leaf.) By exhaustive, there is one rule for each possible attribute-value combination, so
that this set of rules does not require a default rule. Therefore, the order of the rules does not matterthey are
Since we end up with one rule per leaf, the set of extracted rules is not much simpler than the corresponding
tree! The extracted rules may be even more difficult to interpret than the original trees in some cases. As an
example, Figure 6.7 showed decision trees that suffer from subtree repetition and replication. The resulting set of
rules extracted can be large and difficult to follow, because some of the attribute tests may be irrelevant or
redundant. So, the plot thickens. Although it is easy to extract rules from a decision tree, we may need to do some
more work by pruning the resulting rule set.
5.4.3 Rule Induction Using a Sequential Covering Algorithm
IF-THEN rules can be extracted directly from the training data (i.e., without having to generate a decision tree first)
using a Dept.
sequential covering algorithm. The name comes from the notion that the rules are learned
sequentially (one
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at a time), where each rule for a given class will ideally cover many of the tuples of that class (and hopefully none of
the tuples of other classes). Sequential covering algorithms are the most widely used approach to mining disjunctive
sets of classification rules, and form the topic of this subsection. Note that in a newer alternative approach,
classification rules can be generated using associative classification algorithms, which search for attribute-value
pairs that occur frequently in the data. These pairs may form association rules, which can be analyzed and used in
classification. Since this latter approach is based on association rule mining (Chapter 5), we prefer to defer its
treatment until later, in Section 6.8. There are many sequential covering algorithms. Popular variations include AQ,
CN2, and the more recent, RIPPER. The general strategy is as follows. Rules are learned one at a time. Each time a
rule is learned, the tuples covered by the rule are removed, and the process repeats on the remaining tuples. This
sequential learning of rules is in contrast to decision tree induction. Because the path to each leaf in a decision tree
corresponds to a rule, we can consider decision tree induction as learning a set of rules simultaneously.
A basic sequential covering algorithm is shown in Figure 6.12. Here, rules are learned for one class at a time.
Ideally, when learning a rule for a class, Ci, we would like the rule to cover all (or many) of the training tuples of
class C and none (or few) of the tuples from other classes. In this way, the rules learned should be of high accuracy.
The rules need not necessarily be of high coverage.

Algorithm: Sequential covering. Learn a set of IF-THEN rules for classification.

Input: D, a data set class-labeled tuples;
Att vals, the set of all attributes and their possible values.
Output: A set of IF-THEN rules.
(1) Rule set = fg; // initial set of rules learned is empty
(2) for each class c do
(3) repeat
(4) Rule = Learn One Rule(D, Att vals, c);
(5) remove tuples covered by Rule from D;
(6) until terminating condition;
(7) Rule set = Rule set +Rule; // add new rule to rule set
(8) endfor
(9) return Rule Set;

This is because we can have more than one rule for a class, so that different rules may cover different tuples within
the same class. The process continues until the terminating condition is met, such as when there are no more training
tuples or the quality of a rule returned is below a user-specified threshold. The Learn One Rule procedure finds the
best rule for the current class, given the current set of training tuples. How are rules learned? Typically, rules are
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grown in a general-to-specific manner .We can think of this as a beam search, where we start off with an empty rule
and then gradually keep appending attribute tests to it. We append by adding the attribute test as a logical conjunct
to the existing condition of the rule antecedent. Suppose our training set, D, consists of loan application data.
Attributes regarding each applicant include their age, income, education level, residence, credit rating, and the term
of the loan. The classifying attribute is loan decision, which indicates whether a
loan is accepted (considered safe) or rejected (considered risky). To learn a rule for the class accept, we start off
with the most general rule possible, that is, the condition of the rule antecedent is empty. The rule is:
IF THEN loan decision = accept.
We then consider each possible attribute test that may be added to the rule. These can be derived from the parameter
Att vals, which contains a list of attributes with their associated values. For example, for an attribute-value pair (att,
val), we can consider attribute tests such as att = val, att _ val, att > val, and so on. Typically, the training data will
contain many attributes, each of which may have several possible values. Finding an optimal rule set becomes
computationally explosive. Instead, Learn One Rule adopts a greedy depth-first strategy. Each time it is faced with
adding a new attribute test (conjunct) to the current rule, it picks the one that most improves the rule quality,
based on the training samples. We will say more about rule quality measures in a minute. For the moment, lets say
we use rule accuracy as our quality measure. Getting back to our example with Figure 6.13, suppose Learn One Rule
finds that the attribute test income = high best improves the accuracy of our current (empty) rule. We append it to
the condition, so that the current rule becomes
IF income = high THEN loan decision = accept. Each time we add an attribute test to a rule, the resulting rule should
cover more of the accept tuples. During the next iteration, we again consider the possible attribute tests and end up
selecting credit rating = excellent. Our current rule grows to become
IF income = high AND credit rating = excellent THEN loan decision = accept.
The process repeats, wher at each step, we continue to greedily grow rules until the resulting rule meets an
acceptable quality level.

5.4.4 Rule Pruning

Learn One Rule does not employ a test set when evaluating rules. Assessments of rule quality as described above are
made with tuples from the original training data. Such assessment is optimistic because the rules will likely overfit
the data. That is, the rules may perform well on the training data, but less well on subsequent data. To compensate
for this, we can prune the rules. A rule is pruned by removing a conjunct (attribute test). We choose to prune a rule,
R, if the pruned version of R has greater quality, as assessed on an independent set of tuples. As in decision tree
pruning, we
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refer to this set as a pruning set. Various pruning strategies can be used, such as the pessimistic pruning approach
described in the previous section. FOIL uses a simple yet effective method. Given a rule, R,

where pos and neg are the number of positive and negative tuples covered by R, respectively. This value will
increase with the accuracy of R on a pruning set. Therefore, if the FOIL Prune value is higher for the pruned version
of R, then we prune R. By convention, RIPPER starts with the most recently added conjunct when considering
pruning. Conjuncts are pruned one at a time as long as this results in an improvement.

Rule based classifier

Classify records by using a collection of ifthen rules
Rule: (Condition) y
Where Condition is a conjunction of attributes y is the class label
LHS: rule antecedent or condition
RHS: rule consequent
Examples of classification rules:
(Blood Type=Warm) (Lay Eggs=Yes) Birds
(Taxable Income < 50K) (Refund=Yes) Evade=No
R1: (Give Birth = no) (Can Fly = yes) Birds
R2: (Give Birth = no) (Live in Water = yes) Fishes
R3: (Give Birth = yes) (Blood Type = warm) Mammals
R4: (Give Birth = no) (Can Fly = no) Reptiles
R5: (Live in Water = sometimes) Amphibians
Application of Rule-Based Classifier
A rule r covers an instance x if the attributes of the instance satisfy the condition of the rule.


R The rule R1 covers a hawk => Bird

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The rule R3 covers the grizzly bear => Mammal

Rule Coverage and Accuracy
Coverage of a rule: Fraction of records that satisfy the antecedent of a rule
Accuracy of a rule: Fraction of records satisfy both the antecedent and consequent of a rule table

Characteristics of Rule-Based Classifier

Mutually exclusive rules: Classifier contains mutually exclusive rules if the rules are independent of each other
every record is covered by at most one rule.

Exhaustive rules: Classifier has exhaustive coverage if accounts for every possible combination of attribute values
each record is covered by at least one rule.

5.5 Nearest-Neighbor Classifiers

Nearest-neighbor classifiers are based on learning by analogy, that is, by comparing a given test tuplewith training
tuples that are similar to it. The training tuples are described by n attributes. Each tuple represents a point in an n-
dimensional space. In this way all of the training tuples are stored in an n-dimensional pattern space. When given an
unknown tuple, a k-nearest-neighbor classifier searches the pattern space for the k training tuples that are closest to
the unknown tuple. These k training tuples are the k nearest neighbors of the unknown tuple. Closeness is
defined in terms of a distance metric, such as Euclidean distance. The Euclidean distance between two points or
tuples, say, X1 = (x11, x12, : : : , x1n) and X2 = (x21, x22, : : : , x2n), is

In other words, for each numeric attribute, we take the difference between the corresponding values of that attribute
in tuple X1 and in tuple X2, square this difference, and accumulate it. The square root is taken of the total
accumulated distance count. Typically, we normalize the values of each attribute before using Euclids Equation.
This helps prevent attributes with initially large ranges (such as income) from outweighing attributes with initially
smaller ranges (such as binary attributes). Min-max normalization, for example, can be used to transform a value v
of a numeric attribute A to v0 in the range [0, 1] by computing

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where minA and maxA are the minimum and maximum values of attribute A. Chapter 2 describes other methods for
data normalization as a form of data transformation. For k-nearest-neighbor classification, the unknown tuple is
assigned the most common class among its k nearest neighbors. When k = 1, the unknown tuple is assigned the class
of the training tuple that is closest to it in pattern space. Nearest neighbor classifiers can also be used for prediction,
that is, to return a real-valued prediction for a given unknown tuple. In this case, the classifier returns the
average value of the real-valued labels associated with the k nearest neighbors of the unknown tuple. But how can
distance be computed for attributes that not numeric, but categorical, such as color? The above discussion assumes
that the attributes used to describe the tuples are all numeric. For categorical attributes, a simple method is to
compare the corresponding value of the attribute in tuple X1 with that in tuple X2. If the two are identical (e.g.,
tuples X1 and X2
both have the color blue), then the difference between the two is taken as 0. If the two are different (e.g., tuple X1 is
blue but tuple X2 is red), then the difference is considered to be 1. Other methods may incorporate more
sophisticated schemes for differential grading (e.g., where a larger difference score is assigned, say, for blue and
white than for blue and black).
What about missing values? In general, if the value of a given attribute A is missing in tuple X1 and/or in tuple
X2, we assume the maximum possible difference. Suppose that each of the attributes have been mapped to the range
[0, 1]. For categorical attributes, we take the difference value to be 1 if either one or both of the corresponding
values of A are missing. If A is numeric and missing fromboth tuples X1 and X2, then the difference is also taken to
be 1.
How can I determine a good value for k, the number of neighbors? This can be determined experimentally.
Starting with k = 1, we use a test set to estimate the error rate of the classifier. This process can be repeated each
time by incrementing k to allow for one more neighbor. The k value that gives the minimum error rate may be
selected. In general, the larger the number of training tuples is, the larger the value of k will be (so that classification
and prediction decisions can be based on a larger portion of the stored tuples). As the number of training tuples
approaches infinity and k =1, the error rate can be no worse then twice the Bayes error rate (the latter being the
theoretical minimum).
5.6. Introduction to Bayesian Classification
way by determining probabilities of the outcomes. It can solve diagnostic and predictive problems. This
Classification is named after Thomas Bayes ( 1702-1761), who proposed the ayesTheorem.Bayesian classification
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explicit probabilities forhypothesis and it is robust to noise in input data.Uses of Naive Bayes classification: 1. Naive
Bayes text classification) The Bayesian classification is used as a probabilistic learning method (Naive Bayes text
classification). Naive Bayes classifiers are among the most successful known algorithms forlearning to classify text
2. Spam filtering ( filtering is the best known use of
Naive Bayesian text classification. It makes use of anaive Bayes classifier to identify spam e-mail.Bayesian spam
filtering has become a popular mechanism to distinguish illegitimate spamemail from legitimate email (sometimes
called "ham" or "bacn").[4] Many modern mail clientsimplement Bayesian spam filtering. Users can also install
separate email filtering programs.

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Clustering Techniques
Clustering and classification are both fundamental tasks in Data Mining. Classification is used mostly as a
supervised learning method, clustering for unsupervised learning (some clustering models are for both). The
goal of clus- tering is descriptive, that of classification is predictive (Veyssieres and Plant, 1998). Since the
goal of clustering is to discover a new set of categories, the new groups are of interest in themselves, and their
assessment is intrinsic. In classification tasks, however, an important part of the assessment is extrinsic, since
the groups must reflect some reference set of classes. Understanding our world requires conceptualizing the
similarities and differences between the entities that compose it (Tyron and Bailey, 1970).

Clustering groups data instances

into subsets in such a manner that similar instances are g rouped together,
S = Ci and Ci Cj = for i = j. Consequently, any instance in S
while different instances belong to differ-ent groups. The instances are thereby organized into an efficient
representation that characterizes the population being sampled. Formally, the clustering structure is represented
as a set of subsets C = C1 , . . . , Ck of S, such that: i=1 belongs to exactly one and only one subset.
Clustering of objects is as ancient as the human need for describing the salient characteristics of men and
objects and identifying them with a type. Therefore, it embraces various scientific disciplines: from
mathematics and statistics to biology and genetics, each of which uses different terms to describethe topologies
formed using this analysis. From biological taxonomies, to medical syndromes and genetic genotypes
to manufacturing group tech- nology the problem is identical: forming categories of entities and assigning
individuals to the proper groups within it.
Distance Measures
Since clustering is the grouping of similar instances/objects, some sort of measure that can determine whether
two objects are similar or dissimilar is required. There are two main type of measures used to estimate this
relation: distance measures and similarity measures.
Many clustering methods use distance measures to determine the similarity or dissimilarity between any pair of
objects. It is useful to denote the distance between two instances xi and xj as: d(xi ,xj ). A valid distance measure
should be symmetric and obtains its minimum value (usually zero) in case of identical vectors. The distance
measure is called a metric distance measure if it also satisfies the following properties:

1. Triangle inequality d(xi ,xk ) d(xi ,xj ) + d(xj ,xk ) xi ,xj ,xk S.

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2. d(xi ,xj )= 0 xi = xj xi ,xj S.

7.2 Features of cluster analysis

In this section we describe the most well-known clustering algorithms. The main reason for having many
clustering methods is the fact that the notion of cluster is not precisely defined (Estivill-Castro, 2000).
Consequently many
clustering methods have been developed, each of which uses a different in- diction principle. Farley and
Raftery (1998) suggest dividing the clustering methods into two main groups: hierarchical and partitioning
methods. Han
and Kamber (2001) suggest categorizing the methods into additional three main categories: density-based
methods, model-based clustering and grid-based methods. An alternative categorization based on the induction
principle of the various clustering methods is presented in (Estivill-Castro, 2000).

Based Methods
7.4.1 Hierarchical Methods
These methods construct the clusters by recursively partitioning the instances in either a top-down or bottom-
up fashion. These methods can be sub- divided as following:
Agglomerative hierarchical clustering Each object initially represents a cluster of its own. Then clusters are
successively merged until the desired cluster structure is obtained.

Divisive hierarchical clustering All objects initially belong to one cluster. Then the cluster is divided into
sub-clusters, which are successively divided into their own sub-clusters. This process continues until
the desired cluster structure is obtained.

The result of the hierarchical methods is a dendrogram, representing the nested n grouping of objects and
similarity levels at which groupings change. A clustering of the dat objects is obtained by cutting the
dendrogram at the desired similarity level.
The merging or division of clusters is performed according to some similar-ity measure, chosen so as to optimize
some criterion (such as a sum of squares).The hierarchical clustering methods could be further divided according
to the manner that the similarity measure is calculated (Jain et al., 1999):
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Single-link clustering (also called the connectedness, the minimum method or the nearest neighbor method)
methods that consider the distance between two clusters to be equal to the shortest distance from
any member of one cluster to any member of the other cluster. If the data consist of similarities, the
similarity between a pair of clusters is considered to be equal to the greatest similarity from any member of one
cluster to any member of the other cluster (Sneath and Sokal, 1973).
Complete-link clustering (also called the diameter, the maximum method or the furthest neighbor
method) - methods that consider the distance between two clusters to be equal to the longest distance from
any member of one cluster to any member of the other cluster (King,1967).
Average-link clustering (also called minimum variance method) - meth-ods that consider the distance between
two clusters to be equal to the average distance from any member of one cluster to any member of the other
cluster. Such clustering algorithms may be found in (Ward, 1963)and (Murtagh, 1984).The disadvantages of the
single-link clustering and the average-link clustering can be summarized as follows (Guha et al., 1998):Single-
link clustering has a drawback known as the chaining effect: Afew points that form a bridge between two
clusters cause the single-link clustering to unify these two clusters into one. Average-link clustering may cause
elongated clusters to split and for portions of neighboring elongated clusters to merge.
The complete-link clustering methods usually produce more compact clusters and more useful hierarchies than
the single-link clustering methods, yet the single-link methods are more versatile. Generally, hierarchical
methods are
characterized with the following strengths: Versatility The single-link methods, for example, maintain good
performance on data sets containing non-isotropic clusters, including well-separated, chain-like and concentric
Multiple partitions hierarchical methods produce not one partition, but multiple nested partitions, which
allow different users to choose different partitions, according to the desired similarity level. The hierarchical
partition is presented using the dendrogram.
The main disadvantages of the hierarchical methods are:

Inability to scale well The time complexity of hierarchical algorithms is at least O(m2 ) (where m is the total
number of instances), which is non-linear with the number of objects. Clustering a large number of objects
using a hierarchical algorithm is also characterized by huge I/O costs. Hierarchical methods can never undo
what was done previously. Namely there is no back-tracking capability.

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7.4.2 Partitioning Methods

Partitioning methods relocate instances by moving them from one cluster to another, starting from an initial
partitioning. Such methods typically require that the number of clusters will be pre-set by the user. To achieve
global optimality in partitioned-based clustering, an exhaustive enumeration process of all possible partitions is
required. Because this is not feasible, certain greedy heuristics are used in the form of iterative optimization.
Namely, a relocation method iteratively relocates points between the k clusters. The following subsections
present various types of partitioning methods.
7.5 Quality and Validity of Cluster Analysis.

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Web Mining

8.1 Introduction

Web mining - is the application of data mining techniques to discover patterns from the Web. According to analysis
Web structure mining.

8.2 Web Mining

Web content mining is the mining, extraction and integration of useful data, information and knowledge from Web
page content. The heterogeneity and the lack of structure that permeates much of the ever-expanding information
sources on the World Wide Web, such as hypertext documents, makes automated discovery, organization, and
search and indexing tools of the Internet and the World Wide Web such as Lycos, Alta Vista, WebCrawler,
ALIWEB [6], MetaCrawler, and others provide some comfort to users, but they do not generally provide structural
information nor categorize, filter, or interpret documents. In recent years these factors have prompted researchers to
develop more intelligent tools for information retrieval, such as intelligent web agents, as well as to extend database
and data mining techniques to provide a higher level of organization for semi-structured data available on the web.
The agent-based approach to web mining involves the development of sophisticated AI systems that can act
autonomously or semi-autonomously on behalf of a particular user, to discover and organize web-based information.

Web content mining is differentiated from two different points of view:[1] Information Retrieval View and Database
View. R. Kosala et al.[2] summarized the research works done for unstructured data and semi-structured data from
information retrieval view. It shows that most of the researches use bag of words, which is based on the statistics
features. For the semi-structured data, all the works utilize the HTML structures inside the documents and some
utilized the hyperlink structure between the documents for document representation. As for the database view, in
order to have the better information management and querying on the web, the mining always tries to infer the
structure of the web site to transform a web site to become a database.

There are several ways to represent documents; vector space model is typically used. The documents constitute the
whole vector space. If a term t occurs n(D, t) in document D, the t-th coordinate of D is n(D, t) . When the length of
the words in a document goes to , D maxt n(D, t) . This representation does not realize the importance of words
in a document. To resolve this, tf-idf (Term Frequency Times Inverse Document Frequency) is introduced.

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By multi-scanning the document, we can implement feature selection. Under the condition that the category result is
rarely affected, the extraction of feature subset is needed. The general algorithm is to construct an evaluating
function to evaluate the features. As feature set, Information Gain, Cross Entropy, Mutual Information, and Odds
Ratio are usually used. The classifier and pattern analysis methods of text data mining are very similar to traditional
data mining techniques. The usual evaluative merits are Classification Accuracy, Precision, Recall and Information

8.3 Text mining

entity relation modeling (i.e., learning relations between named entities).Text analysis involves information
processing (NLP) and analytical methods.A typical application is to scan a set of documents written in a natural
index with the
information extracted.

8.4 Generalization of Structured Data

An important feature of object-relational and object-oriented databases is their capabilityof storing, accessing, and
How can generalization be performed on such data? Lets start by looking at thegeneralization of set-valued, list-
valued, and sequence-valued attributes.
by (1) generalization of each value in the set to its corresponding higher-level concept, or (2) derivation of the
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weighted average for numerical data, or the major clusters formed by the set. Moreover, generalization can be
performed by applying different generalization operators to explore alternative generalization paths. In this case,
the result of generalization is a heterogeneous set.

8.5 Spatial Data Mining

medical imaging data, and VLSI chip layout data. Spatial databases have many features distinguishing them from
relational databases. They carry topological and/or distance information, usually organized by sophisticated,
multidimensional spatial indexing structures that are accessed by spatial data access methods and often require
spatial reasoning, geometric computation, and spatial knowledge representation techniques.
explicitly stored in spatial databases. Such mining demands an integration of data miningwith spatial database
technologies. It can be used for understanding spatial data, discovering spatial relationships and relationships
between spatial and nonspatial data, constructing spatial knowledge bases, reorganizing spatial databases, and
optimizing spatial queries. It is expected to have wide applications in geographic information systems, geo
marketing, remote sensing, image database exploration, medical imaging, navigation, traffic control, environmental
studies, and many other areas where spatial data are used. A crucial challenge to spatial data mining is the
exploration of efficient spatial data mining techniques due to the huge amount of spatial data and the complexity of
spatial data types and spatial access methods. What about using statistical techniques for spatial data mining?
Statistical spatial data analysis has been a popular approach to analyzing spatial data and exploring geographic
information. The term geo statistics is often associated with continuous geographic space, whereas the term spatial
statistics is often associated with discrete space. In a statistical model that handles non spatial data, one usually
assumes statistical independence among different portions of data. However, different from traditional data sets,
there is no such independence among spatially distributed data because in reality, spatial objects are often
interrelated, or more exactly spatially co-located, in the sense that the closer the two objects are located, the more
likely they share similar properties. For example, nature resource, climate, temperature, and economic situations are
likely to be similar in geographically
closely located regions. People even consider this as the first law of geography: Everything is related to
else, but nearby things are more related than distant things. Such a property of close interdependency across
nearby space leads to the notion of spatial autocorrelation. Based on this notion, spatial statistical modeling methods
have been developed with good success. Spatial data mining will further develop spatial statistical analysis methods

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and extend them for huge amounts of spatial data, with more emphasis on efficiency, scalability, cooperation with
database and data warehouse systems, improved user interaction, and the discovery of new types of knowledge.

8.6 Mining spatio-temporal data

Spatio-temporal data mining is an emerging research area dedicated to the development and application of novel
computational techniques for the analysis of large spatio-temporal databases. The main impulse to research in this
subfield of data mining comes from the large amount of & spatial data made available by GIS, CAD, robotics and
computer vision applications, computational biology, and mobile computing applications; & temporal data obtained
by registering events (e.g., telecommunication or web traffic data) and monitoring processes and workflows.Both the
temporal and spatial dimensions add substantial complexity to data mining tasks.
First of all, the spatial relations, both metric (such as distance) and non-metric (such as topology, direction,
shape, etc.) and the temporal relations (such as before and after) are information bearing and therefore need to be
considered in the data mining methods.
Secondly, some spatial and temporal relations are implicitly defined, that is, they are not explicitly encoded in a
database. These relations must be extracted from the data and there is a trade-off between pre computing them
before the actual mining process starts (eager approach) and computing them on-the-fly when they are actually
needed (lazy approach). Moreover, despite much formalization of space
and time relations available in spatio-temporal reasoning, the extraction of spatial/ temporal relations implicitly
defined in the data introduces some degree of fuzziness that may have a large impact on the results of the data
mining process.

Thirdly, working at the level of stored data, that is, geometric representations (points, lines and regions) for
spatial data or time stamps for temporal data, is often undesirable. For instance, urban planning researchers are
interested in possible relations between two roads, which either cross each other, or run parallel, or can be confluent,
independently of the fact that the two roads are represented by one or
more tuples of a relational table of Blines or Bregions. Therefore, complex transformations are required to
describe the units of analysis at higher conceptual levels, where human-interpretable properties and relations are
expressed. Fourthly, spatial resolution or temporal granularity can have direct impact on the strength of patterns that
can be discovered in the datasets. Interesting patterns are more likely to be discovered at the lowest
resolution/granularity level. On the other hand, large support is more likely to exist at higher levels. Fifthly, many
rules of qualitative reasoning on spatial and temporal data (e.g., transitive properties for temporal relations after and

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before), as well as spatiotemporal ontologies, provide a valuable source of domain independent knowledge that
should be taken into account when generating patterns. How to express these rules and how to integrate spatio-
temporal reasoning mechanisms in data mining systems are still open problems.

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