text:
Data mining
Machine learning and
data mining
|Dimensionality reduction
|Structured prediction
|Anomaly detection
|Neural nets
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD),[1] an interdisciplinary subfield of computer science,[2][3][4] is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.[2] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[2] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[2]
The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself.[5] It also is a buzzword,[6] and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java"[7] (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons.[8] Often the more general terms "(large scale) data analysis", or "analytics" – or when referring to actual methods, artificial intelligence and machine learning – are more appropriate.
The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.
The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
- Etymology 1
Background 2
- Research and evolution 2.1
Process 3
- Pre-processing 3.1
- Data mining 3.2
- Results validation 3.3
- Standards 4
Notable uses 5
- Games 5.1
- Business 5.2
- Science and engineering 5.3
- Human rights 5.4
- Medical data mining 5.5
- Spatial data mining 5.6
- Temporal data mining 5.7
- Sensor data mining 5.8
- Visual data mining 5.9
- Music data mining 5.10
- Surveillance 5.11
- Pattern mining 5.12
- Subject-based data mining 5.13
- Knowledge grid 5.14
Privacy concerns and ethics 6
- Situation in Europe 6.1
- Situation in the United States 6.2
Copyright Law 7
- Situation in Europe 7.1
- Situation in the United States 7.2
Software 8
- Free open-source data mining software and applications 8.1
- Commercial data-mining software and applications 8.2
- Marketplace surveys 8.3
- See also 9
- References 10
- Further reading 11
- External links 12
In the 1960s, statisticians used terms like "Data Fishing" or "Data Dredging" to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "Data Mining" appeared around 1990 in the database community. For a short time in 1980s, a phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego-based company (now merged into FICO), to pitch their Database Mining Workstation;[9] researchers consequently turned to "data mining". Other terms used include Data Archaeology, Information Harvesting, Information Discovery, Knowledge Extraction, etc. Gregory Piatetsky-Shapiro coined the term "Knowledge Discovery in Databases" for the first workshop on the same topic (KDD-1989) and this term became more popular in AI and Machine Learning Community. However, the term data mining became more popular in the business and press communities.[10] Currently, Data Mining and Knowledge Discovery are used interchangeably. Since about 2007, "Predictive Analytics" and since 2011, "Data Science" terms were also used to describe this field.
The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees and decision rules (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns[11] in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever larger data sets.
Research and evolution
The premier professional body in the field is the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining (SIGKDD).[12][13] Since 1989 this ACM SIG has hosted an annual international conference and published its proceedings,[14] and since 1999 it has published a biannual academic journal titled "SIGKDD Explorations".[15]
Computer science conferences on data mining include:
- CIKM Conference – ACM Conference on Information and Knowledge Management
- DMIN Conference – International Conference on Data Mining
- DMKD Conference – Research Issues on Data Mining and Knowledge Discovery
- ECDM Conference – European Conference on Data Mining
- ECML-PKDD Conference – European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
- EDM Conference – International Conference on Educational Data Mining
- ICDM Conference – IEEE International Conference on Data Mining
- KDD Conference – ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- MLDM Conference – Machine Learning and Data Mining in Pattern Recognition
- PAKDD Conference – The annual Pacific-Asia Conference on Knowledge Discovery and Data Mining
- PAW Conference – Predictive Analytics World
- SDM Conference – SIAM International Conference on Data Mining (SIAM)
- SSTD Symposium – Symposium on Spatial and Temporal Databases
- WSDM Conference – ACM Conference on Web Search and Data Mining
Data mining topics are also present on many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases
The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:
- (1) Selection
- (2) Pre-processing
- (3) Transformation
- (4) Data Mining
- (5) Interpretation/Evaluation.[1]
It exists, however, in many variations on this theme, such as the Cross Industry Standard Process for Data Mining (CRISP-DM) which defines six phases:
- (1) Business Understanding
- (2) Data Understanding
- (3) Data Preparation
- (4) Modeling
- (5) Evaluation
- (6) Deployment
or a simplified process such as (1) pre-processing, (2) data mining, and (3) results validation.
Polls conducted in 2002, 2004, and 2007 show that the CRISP-DM methodology is the leading methodology used by data miners.[16][17][18] The only other data mining standard named in these polls was SEMMA. However, 3-4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models,[19][20] and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.[21]
Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.
Data mining
Data mining involves six common classes of tasks:[1]
- Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
- Association rule learning (Dependency modeling) – Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
- Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
- Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".
- Regression – attempts to find a function which models the data with the least error.
- Summarization – providing a more compact representation of the data set, including visualization and report generation.
Results validation
Data mining can unintentionally be misused, and can then produce results which appear to be significant; but which do not actually predict future behavior and cannot be reproduced on a new sample of data and bear little use. Often this results from investigating too many hypotheses and not performing proper statistical hypothesis testing. A simple version of this problem in machine learning is known as overfitting, but the same problem can arise at different phases of the process and thus a train/test split - when applicable at all - may not be sufficient to prevent this from happening.
The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the data mining algorithms are necessarily valid. It is common for the data mining algorithms to find patterns in the training set which are not present in the general data set. This is called overfitting. To overcome this, the evaluation uses a test set of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set, and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish "spam" from "legitimate" emails would be trained on a training set of sample e-mails. Once trained, the learned patterns would be applied to the test set of e-mails on which it had not been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify. A number of statistical methods may be used to evaluate the algorithm, such as ROC curves.
If the learned patterns do not meet the desired standards, subsequently it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge.
There have been some efforts to define standards for the data mining process, for example the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006, but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.
For exchanging the extracted models – in particular for use in predictive analytics – the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover (for example) subspace clustering have been proposed independently of the DMG.[22]
Notable uses
Since the early 1960s, with the availability of oracles for certain combinatorial games, also called tablebases (e.g. for 3x3-chess) with any beginning configuration, small-board dots-and-boxes, small-board-hex, and certain endgames in chess, dots-and-boxes, and hex; a new area for data mining has been opened. This is the extraction of human-usable strategies from these oracles. Current pattern recognition approaches do not seem to fully acquire the high level of abstraction required to be applied successfully. Instead, extensive experimentation with the tablebases – combined with an intensive study of tablebase-answers to well designed problems, and with knowledge of prior art (i.e., pre-tablebase knowledge) – is used to yield insightful patterns. Berlekamp (in dots-and-boxes, etc.) and John Nunn (in chess endgames) are notable examples of researchers doing this work, though they were not – and are not – involved in tablebase generation.
In business, data mining is the analysis of historical business activities, stored as static data in data warehouse databases. The goal is to reveal hidden patterns and trends. Data mining software uses advanced pattern recognition algorithms to sift through large amounts of data to assist in discovering previously unknown strategic business information. Examples of what businesses use data mining for include performing market analysis to identify new product bundles, finding the root cause of manufacturing problems, to prevent customer attrition and acquire new customers, cross-sell to existing customers, and profile customers with more accuracy.[23]
- In today's world raw data is being collected by companies at an exploding rate. For example, Walmart processes over 20 million point-of-sale transactions every day. This information is stored in a centralized database, but would be useless without some type of data mining software to analyze it. If Walmart analyzed their point-of-sale data with data mining techniques they would be able to determine sales trends, develop marketing campaigns, and more accurately predict customer loyalty.[24]
- Every time a credit card or a store loyalty card is being used, or a warranty card is being filled, data is being collected about the users behavior. Many people find the amount of information stored about us from companies, such as Google, Facebook, and Amazon, disturbing and are concerned about privacy. Although there is the potential for our personal data to be used in harmful, or unwanted, ways it is also being used to make our lives better. For example, Ford and Audi hope to one day collect information about customer driving patterns so they can recommend safer routes and warn drivers about dangerous road conditions.[25]
- Data mining in customer relationship management applications can contribute significantly to the bottom line. Rather than randomly contacting a prospect or customer through a call center or sending mail, a company can concentrate its efforts on prospects that are predicted to have a high likelihood of responding to an offer. More sophisticated methods may be used to optimize resources across campaigns so that one may predict to which channel and to which offer an individual is most likely to respond (across all potential offers). Additionally, sophisticated applications could be used to automate mailing. Once the results from data mining (potential prospect/customer and channel/offer) are determined, this "sophisticated application" can either automatically send an e-mail or a regular mail. Finally, in cases where many people will take an action without an offer, "uplift modeling" can be used to determine which people have the greatest increase in response if given an offer. Uplift modeling thereby enables marketers to focus mailings and offers on persuadable people, and not to send offers to people who will buy the product without an offer. Data clustering can also be used to automatically discover the segments or groups within a customer data set.
- Businesses employing data mining may see a return on investment, but also they recognize that the number of predictive models can quickly become very large. For example, rather than using one model to predict how many customers will churn, a business may choose to build a separate model for each region and customer type. In situations where a large number of models need to be maintained, some businesses turn to more automated data mining methodologies.
- Data mining can be helpful to human resources (HR) departments in identifying the characteristics of their most successful employees. Information obtained – such as universities attended by highly successful employees – can help HR focus recruiting efforts accordingly. Additionally, Strategic Enterprise Management applications help a company translate corporate-level goals, such as profit and margin share targets, into operational decisions, such as production plans and workforce levels.[26]
- Market basket analysis, relates to data-mining use in retail sales. If a clothing store records the purchases of customers, a data mining system could identify those customers who favor silk shirts over cotton ones. Although some explanations of relationships may be difficult, taking advantage of it is easier. The example deals with association rules within transaction-based data. Not all data are transaction based and logical, or inexact rules may also be present within a database.
- Market basket analysis has been used to identify the purchase patterns of the Alpha Consumer. Analyzing the data collected on this type of user has allowed companies to predict future buying trends and forecast supply demands.
- Data mining is a highly effective tool in the catalog marketing industry. Catalogers have a rich database of history of their customer transactions for millions of customers dating back a number of years. Data mining tools can identify patterns among customers and help identify the most likely customers to respond to upcoming mailing campaigns.
- Data mining for business applications can be integrated into a complex modeling and decision making process.[27] Reactive business intelligence (RBI) advocates a "holistic" approach that integrates data mining, modeling, and interactive visualization into an end-to-end discovery and continuous innovation process powered by human and automated learning.[28]
- In the area of [30] for effectively monitoring and controlling the semiconductor wafer fabrication line were developed.decision rules was applied to a real-world semiconductor wafer manufacturing line, where [27] This decision-theoretic classification framework[27]
- An example of data mining related to an integrated-circuit (IC) production line is described in the paper "Mining IC Test Data to Optimize VLSI Testing."[31] In this paper, the application of data mining and decision analysis to the problem of die-level functional testing is described. Experiments mentioned demonstrate the ability to apply a system of mining historical die-test data to create a probabilistic model of patterns of die failure. These patterns are then utilized to decide, in real time, which die to test next and when to stop testing. This system has been shown, based on experiments with historical test data, to have the potential to improve profits on mature IC products. Other examples[32][33] of the application of data mining methodologies in semiconductor manufacturing environments suggest that data mining methodologies may be particularly useful when data is scarce, and the various physical and chemical parameters that affect the process exhibit highly complex interactions. Another implication is that on-line monitoring of the semiconductor manufacturing process using data mining may be highly effective.
Science and engineering
In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, education and electrical power engineering.
- In the study of human genetics, sequence mining helps address the important goal of understanding the mapping relationship between the inter-individual variations in human DNA sequence and the variability in disease susceptibility. In simple terms, it aims to find out how the changes in an individual's DNA sequence affects the risks of developing common diseases such as cancer, which is of great importance to improving methods of diagnosing, preventing, and treating these diseases. One data mining method that is used to perform this task is known as multifactor dimensionality reduction.[34]
- In the area of electrical power engineering, data mining methods have been widely used for [35]
- Data mining methods have been applied to dissolved gas analysis (DGA) in power transformers. DGA, as a diagnostics for power transformers, has been available for many years. Methods such as SOM has been applied to analyze generated data and to determine trends which are not obvious to the standard DGA ratio methods (such as Duval Triangle).[35]
- In educational research, where data mining has been used to study the factors leading students to choose to engage in behaviors which reduce their learning,[36] and to understand factors influencing university student retention.[37] A similar example of social application of data mining is its use in expertise finding systems, whereby descriptors of human expertise are extracted, normalized, and classified so as to facilitate the finding of experts, particularly in scientific and technical fields. In this way, data mining can facilitate institutional memory.
- Data mining methods of biomedical data facilitated by domain ontologies,[38] mining clinical trial data,[39] and traffic analysis using SOM.[40]
- In adverse drug reaction surveillance, the Uppsala Monitoring Centre has, since 1998, used data mining methods to routinely screen for reporting patterns indicative of emerging drug safety issues in the WHO global database of 4.6 million suspected adverse drug reaction incidents.[41] Recently, similar methodology has been developed to mine large collections of electronic health records for temporal patterns associating drug prescriptions to medical diagnoses.[42]
- Data mining has been applied to software artifacts within the realm of software engineering: Mining Software Repositories.
Human rights
Data mining of government records – particularly records of the justice system (i.e., courts, prisons) – enables the discovery of systemic human rights violations in connection to generation and publication of invalid or fraudulent legal records by various government agencies.[43][44]
Medical data mining
In 2011, the case of Sorrell v. IMS Health, Inc., decided by the Supreme Court of the United States, ruled that pharmacies may share information with outside companies. This practice was authorized under the 1st Amendment of the Constitution, protecting the "freedom of speech."[45] However, the passage of the Health Information Technology for Economic and Clinical Health Act (HITECH Act) helped to initiate the adoption of the electronic health record (EHR) and supporting technology in the United States.[46] The HITECH Act was signed into law on February 17, 2009 as part of the American Recovery and Reinvestment Act (ARRA) and helped to open the door to medical data mining.[47] Prior to the signing of this law, estimates of only 20% of United States based physician were utilizing electronic patient records.[46] Søren Brunak notes that "the patient record becomes as information-rich as possible" and thereby "maximizes the data mining opportunities."[46] Hence, electronic patient records further expands the possibilities regarding medical data mining thereby opening the door to a vast source of medical data analysis.
Spatial data mining
Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to visualization and data analysis. Particularly, most contemporary GIS have only very basic spatial analysis functionality. The immense explosion in geographically referenced data occasioned by developments in IT, digital mapping, remote sensing, and the global diffusion of GIS emphasizes the importance of developing data-driven inductive approaches to geographical analysis and modeling.
Data mining offers great potential benefits for GIS-based applied decision-making. Recently, the task of integrating these two technologies has become of critical importance, especially as various public and private sector organizations possessing huge databases with thematic and geographically referenced data begin to realize the huge potential of the information contained therein. Among those organizations are:
- offices requiring analysis or dissemination of geo-referenced statistical data
- public health services searching for explanations of disease clustering
- environmental agencies assessing the impact of changing land-use patterns on climate change
- geo-marketing companies doing customer segmentation based on spatial location.
Challenges in Spatial mining: Geospatial data repositories tend to be very large. Moreover, existing GIS datasets are often splintered into feature and attribute components that are conventionally archived in hybrid data management systems. Algorithmic requirements differ substantially for relational (attribute) data management and for topological (feature) data management.[48] Related to this is the range and diversity of geographic data formats, which present unique challenges. The digital geographic data revolution is creating new types of data formats beyond the traditional "vector" and "raster" formats. Geographic data repositories increasingly include ill-structured data, such as imagery and geo-referenced multi-media.[49]
There are several critical research challenges in geographic knowledge discovery and data mining. Miller and Han[50] offer the following list of emerging research topics in the field:
- Developing and supporting geographic data warehouses (GDW's): Spatial properties are often reduced to simple aspatial attributes in mainstream data warehouses. Creating an integrated GDW requires solving issues of spatial and temporal data interoperability – including differences in semantics, referencing systems, geometry, accuracy, and position.
- Better spatio-temporal representations in geographic knowledge discovery: Current geographic knowledge discovery (GKD) methods generally use very simple representations of geographic objects and spatial relationships. Geographic data mining methods should recognize more complex geographic objects (i.e., lines and polygons) and relationships (i.e., non-Euclidean distances, direction, connectivity, and interaction through attributed geographic space such as terrain). Furthermore, the time dimension needs to be more fully integrated into these geographic representations and relationships.
- Geographic knowledge discovery using diverse data types: GKD methods should be developed that can handle diverse data types beyond the traditional raster and vector models, including imagery and geo-referenced multimedia, as well as dynamic data types (video streams, animation).
Temporal data mining
Data may contain attributes generated and recorded at different times. In this case finding meaningful relationships in the data may require considering the temporal order of the attributes. A temporal relationship may indicate a causal relationship, or simply an association.
Sensor data mining
Wireless sensor networks can be used for facilitating the collection of data for spatial data mining for a variety of applications such as air pollution monitoring.[51] A characteristic of such networks is that nearby sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires the techniques for in-network data aggregation and mining. By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms.[52]
Visual data mining
In the process of turning from analogical into digital, large data sets have been generated, collected, and stored discovering statistical patterns, trends and information which is hidden in data, in order to build predictive patterns. Studies suggest visual data mining is faster and much more intuitive than is traditional data mining.[53][54][55] See also Computer vision.
Music data mining
Data mining techniques, and in particular co-occurrence analysis, has been used to discover relevant similarities among music corpora (radio lists, CD databases) for purposes including classifying music into genres in a more objective manner.[56]
Data mining has been used by the U.S. government. Programs include the
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- Data mining
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External links
- Cabena, Peter; Hadjnian, Pablo; Stadler, Rolf; Verhees, Jaap; and Zanasi, Alessandro (1997); Discovering Data Mining: From Concept to Implementation, Prentice Hall, ISBN 0-13-743980-6
- M.S. Chen, J. Han, P.S. Yu (1996) "Data mining: an overview from a database perspective". Knowledge and data Engineering, IEEE Transactions on 8 (6), 866-883
- Feldman, Ronen; and Sanger, James; The Text Mining Handbook, Cambridge University Press, ISBN 978-0-521-83657-9
- Guo, Yike; and Grossman, Robert (editors) (1999); High Performance Data Mining: Scaling Algorithms, Applications and Systems, Kluwer Academic Publishers
- Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques. Morgan kaufmann, 2006.
- Hastie, Trevor, Tibshirani, Robert and Friedman, Jerome (2001); The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, ISBN 0-387-95284-5
- Liu, Bing (2007); Web Data Mining: Exploring Hyperlinks, Contents and Usage Data, Springer, ISBN 3-540-37881-2
- Murphy, Chris (16 May 2011). "Is Data Mining Free Speech?".
- Nisbet, Robert; Elder, John; Miner, Gary (2009); Handbook of Statistical Analysis & Data Mining Applications, Academic Press/Elsevier, ISBN 978-0-12-374765-5
- Poncelet, Pascal; Masseglia, Florent; and Teisseire, Maguelonne (editors) (October 2007); "Data Mining Patterns: New Methods and Applications", Information Science Reference, ISBN 978-1-59904-162-9
- Tan, Pang-Ning; Steinbach, Michael; and Kumar, Vipin (2005); Introduction to Data Mining, ISBN 0-321-32136-7
- Theodoridis, Sergios; and Koutroumbas, Konstantinos (2009); Pattern Recognition, 4th Edition, Academic Press, ISBN 978-1-59749-272-0
- Weiss, Sholom M.; and Indurkhya, Nitin (1998); Predictive Data Mining, Morgan Kaufmann
- Witten, Ian H.; Frank, Eibe; Hall, Mark A. (30 January 2011). Data Mining: Practical Machine Learning Tools and Techniques (3 ed.). Elsevier. (See also Free Weka software)
- Ye, Nong (2003); The Handbook of Data Mining, Mahwah, NJ: Lawrence Erlbaum
Further reading
- ^ a b c Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996). "From Data Mining to Knowledge Discovery in Databases". Retrieved 17 December 2008.
- ^ a b c d "Data Mining Curriculum".
- ^ Clifton, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining". Retrieved 2010-12-09.
- ^ Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction". Retrieved 2012-08-07.
- ^ Han, Jiawei; Kamber, Micheline (2001). Data mining: concepts and techniques.
- ^ See e.g. OKAIRP 2005 Fall Conference, Arizona State University, About.com: Datamining
- ^ Witten, Ian H.; Frank, Eibe; Hall, Mark A. (30 January 2011). Data Mining: Practical Machine Learning Tools and Techniques (3 ed.). Elsevier.
^ Bouckaert, Remco R.; Frank, Eibe; Hall, Mark A.; Holmes, Geoffrey; Pfahringer, Bernhard; Reutemann, Peter; Witten, Ian H. (2010). "WEKA Experiences with a Java open-source project". Journal of Machine Learning Research 11: 2533–2541.
the original title, "Practical machine learning", was changed ... The term "data mining" was [added] primarily for marketing reasons.
- ^ Mena, Jesús (2011). Machine Learning Forensics for Law Enforcement, Security, and Intelligence. Boca Raton, FL: CRC Press (Taylor & Francis Group).
- ^ Piatetsky-Shapiro, Gregory; Parker, Gary (2011). "Lesson: Data Mining, and Knowledge Discovery: An Introduction". Introduction to Data Mining. KD Nuggets. Retrieved 30 August 2012.
- ^ Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons.
- ^ "Microsoft Academic Search: Top conferences in data mining".
- ^ "Google Scholar: Top publications - Data Mining & Analysis".
- ^ Proceedings, International Conferences on Knowledge Discovery and Data Mining, ACM, New York.
- ^ SIGKDD Explorations, ACM, New York.
- ^ Gregory Piatetsky-Shapiro (2002) KDnuggets Methodology Poll
- ^ Gregory Piatetsky-Shapiro (2004) KDnuggets Methodology Poll
- ^ Gregory Piatetsky-Shapiro (2007) KDnuggets Methodology Poll
- ^ Óscar Marbán, Gonzalo Mariscal and Javier Segovia (2009); A Data Mining & Knowledge Discovery Process Model. In Data Mining and Knowledge Discovery in Real Life Applications, Book edited by: Julio Ponce and Adem Karahoca, ISBN 978-3-902613-53-0, pp. 438–453, February 2009, I-Tech, Vienna, Austria.
- ^ Lukasz Kurgan and Petr Musilek (2006); A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review. Volume 21 Issue 1, March 2006, pp 1–24, Cambridge University Press, New York, NY, USA doi:10.1017/S0269888906000737
- ^ Azevedo, A. and Santos, M. F. KDD, SEMMA and CRISP-DM: a parallel overview. In Proceedings of the IADIS European Conference on Data Mining 2008, pp 182–185.
- ^ Günnemann, Stephan; Kremer, Hardy; Seidl, Thomas (2011). "Proceedings of the 2011 workshop on Predictive markup language modeling - PMML '11". p. 48.
- ^ O'Brien, J. A., & Marakas, G. M. (2011). Management Information Systems. New York, NY: McGraw-Hill/Irwin.
- ^ Alexander, D. (n.d.). Data Mining. Retrieved from The University of Texas at Austin: College of Liberal Arts: http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/
- ^ Goss, S. (2013, April 10). Data-mining and our personal privacy. Retrieved from The Telegraph: http://www.macon.com/2013/04/10/2429775/data-mining-and-our-personal-privacy.html
- ^ Monk, Ellen; Wagner, Bret (2006). Concepts in Enterprise Resource Planning, Second Edition. Boston, MA: Thomson Course Technology.
- ^ a b c Elovici, Yuval; Braha, Dan (2003). "A Decision-Theoretic Approach to Data Mining" (PDF). IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 33 (1).
- ^ Battiti, Roberto; and Brunato, Mauro; Reactive Business Intelligence. From Data to Models to Insight, Reactive Search Srl, Italy, February 2011. ISBN 978-88-905795-0-9.
- ^ Battiti, Roberto; Passerini, Andrea (2010). "Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker". IEEE Transactions on Evolutionary Computation 14 (15): 671–687.
- ^ Braha, Dan; Elovici, Yuval; Last, Mark (2007). "Theory of actionable data mining with application to semiconductor manufacturing control" (PDF). International Journal of Production Research 45 (13).
- ^ Fountain, Tony; Dietterich, Thomas; and Sudyka, Bill (2000); Mining IC Test Data to Optimize VLSI Testing, in Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM Press, pp. 18–25
- ^ Braha, Dan; Shmilovici, Armin (2002). "Data Mining for Improving a Cleaning Process in the Semiconductor Industry" (PDF). IEEE Transactions on Semiconductor Manufacturing 15 (1).
- ^ Braha, Dan; Shmilovici, Armin (2003). "On the Use of Decision Tree Induction for Discovery of Interactions in a Photolithographic Process" (PDF). IEEE Transactions on Semiconductor Manufacturing 16 (4).
- ^ Zhu, Xingquan; Davidson, Ian (2007). Knowledge Discovery and Data Mining: Challenges and Realities. New York, NY: Hershey. p. 18.
- ^ a b McGrail, Anthony J.; Gulski, Edward; Allan, David; Birtwhistle, David; Blackburn, Trevor R.; Groot, Edwin R. S. "Data Mining Techniques to Assess the Condition of High Voltage Electrical Plant". CIGRÉ WG 15.11 of Study Committee 15.
- ^ Baker, Ryan S. J. d. "Is Gaming the System State-or-Trait? Educational Data Mining Through the Multi-Contextual Application of a Validated Behavioral Model". Workshop on Data Mining for User Modeling 2007.
- ^ Superby Aguirre, Juan Francisco; Vandamme, Jean-Philippe; Meskens, Nadine. "Determination of factors influencing the achievement of the first-year university students using data mining methods". Workshop on Educational Data Mining 2006.
- ^ Zhu, Xingquan; Davidson, Ian (2007). Knowledge Discovery and Data Mining: Challenges and Realities. New York, NY: Hershey. pp. 163–189.
- ^ Zhu, Xingquan; Davidson, Ian (2007). Knowledge Discovery and Data Mining: Challenges and Realities. New York, NY: Hershey. pp. 31–48.
- ^ Chen, Yudong; Zhang, Yi; Hu, Jianming; Li, Xiang (2006). "Traffic Data Analysis Using Kernel PCA and Self-Organizing Map". IEEE Intelligent Vehicles Symposium.
- ^ Bate, Andrew; Lindquist, Marie; Edwards, I. Ralph; Olsson, Sten; Orre, Roland; Lansner, Anders; and de Freitas, Rogelio Melhado; A Bayesian neural network method for adverse drug reaction signal generation, European Journal of Clinical Pharmacology 1998 Jun; 54(4):315–21 PubMed
- ^ Norén, G. Niklas; Bate, Andrew; Hopstadius, Johan; Star, Kristina; and Edwards, I. Ralph (2008); Temporal Pattern Discovery for Trends and Transient Effects: Its Application to Patient Records. Proceedings of the Fourteenth International Conference on Knowledge Discovery and Data Mining (SIGKDD 2008), Las Vegas, NV, pp. 963–971.
- ^ Zernik, Joseph; Data Mining as a Civic Duty – Online Public Prisoners' Registration Systems, International Journal on Social Media: Monitoring, Measurement, Mining, 1: 84–96 (2010)
- ^ Zernik, Joseph; Data Mining of Online Judicial Records of the Networked US Federal Courts, International Journal on Social Media: Monitoring, Measurement, Mining, 1:69–83 (2010)
- ^ David G. Savage (2011-06-24). "Pharmaceutical industry: Supreme Court sides with pharmaceutical industry in two decisions". Los Angeles Times. Retrieved 2012-11-07.
- ^ a b c Analyzing Medical Data. (2012). Communications of the ACM 55(6), 13-15. doi:10.1145/2184319.2184324
- ^ http://searchhealthit.techtarget.com/definition/HITECH-Act
- ^ Healey, Richard G. (1991); Database Management Systems, in Maguire, David J.; Goodchild, Michael F.; and Rhind, David W., (eds.), Geographic Information Systems: Principles and Applications, London, GB: Longman
- ^ Camara, Antonio S.; and Raper, Jonathan (eds.) (1999); Spatial Multimedia and Virtual Reality, London, GB: Taylor and Francis
- ^ Miller, Harvey J.; and Han, Jiawei (eds.) (2001); Geographic Data Mining and Knowledge Discovery, London, GB: Taylor & Francis
- ^ Ma, Y.; Richards, M.; Ghanem, M.; Guo, Y.; Hassard, J. (2008). "Air Pollution Monitoring and Mining Based on Sensor Grid in London". Sensors 8 (6): 3601.
- ^ Ma, Y.; Guo, Y.; Tian, X.; Ghanem, M. (2011). "Distributed Clustering-Based Aggregation Algorithm for Spatial Correlated Sensor Networks". IEEE Sensors Journal 11 (3): 641.
- ^ Zhao, Kaidi; and Liu, Bing; Tirpark, Thomas M.; and Weimin, Xiao; A Visual Data Mining Framework for Convenient Identification of Useful Knowledge
- ^ Keim, Daniel A.; Information Visualization and Visual Data Mining
- ^ Burch, Michael; Diehl, Stephan; Weißgerber, Peter; Visual Data Mining in Software Archives
- ^ Pachet, François; Westermann, Gert; and Laigre, Damien; Musical Data Mining for Electronic Music Distribution, Proceedings of the 1st WedelMusic Conference,Firenze, Italy, 2001, pp. 101–106.
- ^ Government Accountability Office, Data Mining: Early Attention to Privacy in Developing a Key DHS Program Could Reduce Risks, GAO-07-293 (February 2007), Washington, DC
- ^ Secure Flight Program report, MSNBC
- ^ "Total/Terrorism Information Awareness (TIA): Is It Truly Dead?". Electronic Frontier Foundation (official website). 2003. Retrieved 2009-03-15.
- ^ Agrawal, Rakesh; Mannila, Heikki; Srikant, Ramakrishnan; Toivonen, Hannu; and Verkamo, A. Inkeri; Fast discovery of association rules, in Advances in knowledge discovery and data mining, MIT Press, 1996, pp. 307–328
- ^ a b National Research Council, Protecting Individual Privacy in the Struggle Against Terrorists: A Framework for Program Assessment, Washington, DC: National Academies Press, 2008
- ^ Haag, Stephen; Cummings, Maeve; Phillips, Amy (2006). Management Information Systems for the information age. Toronto: McGraw-Hill Ryerson. p. 28.
- ^ Ghanem, Moustafa; Guo, Yike; Rowe, Anthony; Wendel, Patrick (2002). "Proceedings 11th IEEE International Symposium on High Performance Distributed Computing". p. 416.
- ^ Ghanem, Moustafa; Curcin, Vasa; Wendel, Patrick; Guo, Yike (2009). "Data Mining Techniques in Grid Computing Environments". p. 119.
- ^ Cannataro, Mario; Talia, Domenico (January 2003). "The Knowledge Grid: An Architecture for Distributed Knowledge Discovery". Communications of the ACM 46 (1): 89–93.
- ^ Talia, Domenico; Trunfio, Paolo (July 2010). "How distributed data mining tasks can thrive as knowledge services". Communications of the ACM 53 (7): 132–137.
- ^ Seltzer, William. "The Promise and Pitfalls of Data Mining: Ethical Issues".
- ^ Pitts, Chip (15 March 2007). "The End of Illegal Domestic Spying? Don't Count on It". Washington Spectator.
- ^ Taipale, Kim A. (15 December 2003). "Data Mining and Domestic Security: Connecting the Dots to Make Sense of Data". Columbia Science and Technology Law Review 5 (2).
- ^ Resig, John; and Teredesai, Ankur (2004). "A Framework for Mining Instant Messaging Services". Proceedings of the 2004 SIAM DM Conference.
- ^ a b c Think Before You Dig: Privacy Implications of Data Mining & Aggregation, NASCIO Research Brief, September 2004
- ^ Ohm, Paul. "Don't Build a Database of Ruin". Harvard Business Review.
- ^ Darwin Bond-Graham, Iron Cagebook - The Logical End of Facebook's Patents, Counterpunch.org, 2013.12.03
- ^ Darwin Bond-Graham, Inside the Tech industry's Startup Conference, Counterpunch.org, 2013.09.11
- ^ AOL search data identified individuals, SecurityFocus, August 2006
- ^ Biotech Business Week Editors (June 30, 2008); BIOMEDICINE; HIPAA Privacy Rule Impedes Biomedical Research, Biotech Business Week, retrieved 17 November 2009 from LexisNexis Academic
- ^ Researchers Given Data Mining Right Under New UK Copyright Laws. Out-Law.com. Retrieved 14 November 2014
- ^ "Licences for Europe - Structured Stakeholder Dialogue 2013". European Commission. Retrieved 14 November 2014.
- ^ "Text and Data Mining:Its importance and the need for change in Europe". Association of European Research Libraries. Retrieved 14 November 2014.
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- ^ Kobielus, James; The Forrester Wave: Predictive Analytics and Data Mining Solutions, Q1 2010, Forrester Research, 1 July 2008
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- ^ Goebel, Michael; Gruenwald, Le (1999); A Survey of Data Mining and Knowledge Discovery Software Tools, SIGKDD Explorations, Vol. 1, Issue 1, pp. 20–33
Data mining is about analyzing data; for information about extracting information out of data, see:
- Related topics
- Application examples
- Application domains
- Methods
See also
- 2011 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery[81]
- Rexer Analytics Data Miner Surveys (2007–2013)[82]
- Forrester Research 2010 Predictive Analytics and Data Mining Solutions report[83]
- Gartner 2008 "Magic Quadrant" report[84]
- Robert A. Nisbet's 2006 Three Part Series of articles "Data Mining Tools: Which One is Best For CRM?"[85]
- Haughton et al.'s 2003 Review of Data Mining Software Packages in The American Statistician[86]
- Goebel & Gruenwald 1999 "A Survey of Data Mining a Knowledge Discovery Software Tools" in SIGKDD Explorations[87]
Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. Some of these reports include:
Marketplace surveys
- Angoss KnowledgeSTUDIO: data mining tool provided by Angoss.
- Clarabridge: enterprise class text analytics solution.
- HP Vertica Analytics Platform: data mining software provided by HP.
- IBM SPSS Modeler: data mining software provided by IBM.
- KXEN Modeler: data mining tool provided by KXEN.
- LIONsolver: an integrated software application for data mining, business intelligence, and modeling that implements the Learning and Intelligent OptimizatioN (LION) approach.
- Microsoft Analysis Services: data mining software provided by Microsoft.
- NetOwl: suite of multilingual text and entity analytics products that enable data mining.
- Oracle Data Mining: data mining software by Oracle.
- SAS Enterprise Miner: data mining software provided by the SAS Institute.
- STATISTICA Data Miner: data mining software provided by StatSoft.
- Qlucore Omics Explorer: data mining software provided by Qlucore.
Commercial data-mining software and applications
- Carrot2: Text and search results clustering framework.
- Chemicalize.org: A chemical structure miner and web search engine.
- ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language.
- GATE: a natural language processing and language engineering tool.
- KNIME: The Konstanz Information Miner, a user friendly and comprehensive data analytics framework.
- ML-Flex: A software package that enables users to integrate with third-party machine-learning packages written in any programming language, execute classification analyses in parallel across multiple computing nodes, and produce HTML reports of classification results.
- MLPACK library: a collection of ready-to-use machine learning algorithms written in the C++ language.
- Massive Online Analysis (MOA): a real-time big data stream mining with concept drift tool in the Java programming language.
- NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python language.
- OpenNN: Open neural networks library.
- Orange: A component-based data mining and machine learning software suite written in the Python language.
- R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project.
- RapidMiner: An environment for machine learning and data mining experiments.
- SCaViS: Java cross-platform data analysis framework developed at Argonne National Laboratory.
- SenticNet API: A semantic and affective resource for opinion mining and sentiment analysis.
- Tanagra: A visualisation-oriented data mining software, also for teaching.
- Torch: An open source deep learning library for the Lua programming language and scientific computing framework with wide support for machine learning algorithms.
- UIMA: The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video – originally developed by IBM.
- Weka: A suite of machine learning software applications written in the Java programming language.
Free open-source data mining software and applications
By contrast to Europe, the flexible nature of US copyright law, and in particular fair use means that content mining in America, as well as other fair use countries such as Israel, Taiwan and South Korea is viewed as being legal. As content mining is transformative, that is it does not supplant the original work, it is viewed as being lawful under fair use. For example as part of the Google Book settlement the presiding judge on the case ruled that Google's digitisation project of in-copyright books was lawful, in part because of the transformative uses that the digitisation project displayed - one being text and data mining.[80]
Situation in the United States
Due to a lack of flexibilities in European copyright and database law, the mining of in-copyright works such as web mining without the permission of the copyright owner is not legal. Where a database is pure data in Europe there is likely to be no copyright, but database rights may exist so data mining becomes subject to regulations by the Database Directive. On the recommendation of the Hargreaves review this led to the UK government to amend its copyright law in 2014[77] to allow content mining as a limitation and exception. Only the second country in the world to do so after Japan, which introduced an exception in 2009 for data mining. However due to the restriction of the Copyright Directive, the UK exception only allows content mining for non-commercial purposes. UK copyright law also does not allow this provision to be overridden by contractual terms and conditions. The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe.[78] The focus on the solution to this legal issue being licences and not limitations and exceptions led to representatives of universities, researchers, libraries, civil society groups and open access publishers to leave the stakeholder dialogue in May 2013.[79]
Situation in Europe
Copyright Law
U.S. information privacy legislation such as HIPAA and the Family Educational Rights and Privacy Act (FERPA) applies only to the specific areas that each such law addresses. Use of data mining by the majority of businesses in the U.S. is not controlled by any legislation.
In the United States, privacy concerns have been addressed to some extent by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA). The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses. According to an article in Biotech Business Week', "'[i]n practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,' says the AAHC. More importantly, the rule's goal of protection through informed consent is undermined by the complexity of consent forms that are required of patients and participants, which approach a level of incomprehensibility to average individuals."[76] This underscores the necessity for data anonymity in data aggregation and mining practices.
Situation in the United States
Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers. However, the U.S.-E.U. Safe Harbor Principles currently effectively expose European users to privacy exploitation by U.S. companies. As a consequence of Edward Snowden's Global surveillance disclosure, there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to the National Security Agency, and attempts to reach an agreement have failed.
Situation in Europe
Data may also be modified so as to become anonymous, so that individuals may not readily be identified.[71] However, even "de-identified"/"anonymized" data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.[75]
- the purpose of the data collection and any (known) data mining projects;
- how the data will be used;
- who will be able to mine the data and use the data and their derivatives;
- the status of security surrounding access to the data;
- how collected data can be updated.
It is recommended that an individual is made aware of the following before data are collected:[71]
Data mining requires data preparation which can uncover information or patterns which may compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation involves combining data together (possibly from various sources) in a way that facilitates analysis (but that also might make identification of private, individual-level data deducible or otherwise apparent).[71] This is not data mining per se, but a result of the preparation of data before – and for the purposes of – the analysis. The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous.[72][73][74]
The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics.[68] In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.[69][70]
While the term "data mining" itself has no ethical implications, it is often associated with the mining of information in relation to peoples' behavior (ethical and otherwise).[67]
Privacy concerns and ethics
Knowledge discovery "On the Grid" generally refers to conducting knowledge discovery in an open environment using grid computing concepts, allowing users to integrate data from various online data sources, as well make use of remote resources, for executing their data mining tasks. The earliest example was the Discovery Net,[63][64] developed at Imperial College London, which won the "Most Innovative Data-Intensive Application Award" at the ACM SC02 (Supercomputing 2002) conference and exhibition, based on a demonstration of a fully interactive distributed knowledge discovery application for a bioinformatics application. Other examples include work conducted by researchers at the University of Calabria, who developed a Knowledge Grid architecture for distributed knowledge discovery, based on grid computing.[65][66]
Knowledge grid
"Subject-based data mining" is a data mining method involving the search for associations between individuals in data. In the context of combating terrorism, the National Research Council provides the following definition: "Subject-based data mining uses an initiating individual or other datum that is considered, based on other information, to be of high interest, and the goal is to determine what other persons or financial transactions or movements, etc., are related to that initiating datum."[61]
Subject-based data mining
In the context of pattern mining as a tool to identify terrorist activity, the National Research Council provides the following definition: "Pattern-based data mining looks for patterns (including anomalous data patterns) that might be associated with terrorist activity — these patterns might be regarded as small signals in a large ocean of noise."[60][61][62] Pattern Mining includes new areas such a Music Information Retrieval (MIR) where patterns seen both in the temporal and non temporal domains are imported to classical knowledge discovery search methods.
"Pattern mining" is a data mining method that involves finding existing patterns in data. In this context patterns often means association rules. The original motivation for searching association rules came from the desire to analyze supermarket transaction data, that is, to examine customer behavior in terms of the purchased products. For example, an association rule "beer ⇒ potato chips (80%)" states that four out of five customers that bought beer also bought potato chips.
Pattern mining
In the context of combating terrorism, two particularly plausible methods of data mining are "pattern mining" and "subject-based data mining".


text:
I use this blog for a couple of reasons. Mostly it's to hear myself "talk" -- and see the error of my ways, of which there are plenty -- and get feedback from you, dear generous reader. The other way I use it is to catch a cousin. I get about one every couple of months and I've been happy with that rate of return. But I think it can be better.
When lost or unknown to me cousins contact me it's a trip! We furiously compare trees, exchange packets of info in the mail, and email a bunch of photos to share. It's some serious genealogy fun! Being a fun junkie I want more: that's what junkies do;) So I want to build better bait so as to attract more cousins.
My Grandpa Williams, Mom's dad, was a fisherman who made his own hand-tied flies. You have to think like a fish, he said. So let me try to think like a cousin and see what I come up with.
How can I increase searchability so that when my cousin googles grandpa Camey Williams, the fisherman, they are going to find me? Right now I'm using those labels Blogger provides and includes below the blog post... and I have to remember to use as many as appropriate and Blogger allows so that they get picked up by the search engines.
I wonder if google will pick up grandpa if I just put his info here in the text: Cambria Williams (1897 - 1960)? And then what happens if I add a hyperlink in a new window to Mom's Ancestry page for him? Does that help catch a cousin? Do more links out get me higher on the search results page? Gosh if you know please won't you post a comment?
And do I remember something about Alt tags being useful if you know how to use them? I just for the first time used the Alt tag on the photos below. Maybe I should have been doing that all along.
Anything else I'm missing here to improve searchability?
Maybe it's just me, but I love photos. Could have jumped right through the screen and hugged that distant cousin who posted a photo of my great great grandmother, Mary Myers (1837 - 1909) on facebook. Here it is below. She was the granddaughter of my Revolutionary War ancestor, Nehemiah Newans (1740 - 1820) whom you can find on Mom's Big Tree on Ancestry at: http://trees.ancestry.com/tree/18168528/person/19373593834
Place names.
My ancestors came from such specific places that I think maybe I could be using those too to catch a cousin. Places like Frostburg, Maryland, a town of just 8,000 people in 2000, and Magnolia, West Virginia, now gone entirely. You wouldn't be searching Magnolia unless you have an ancestor who lived there, and if so there's maybe a one out of two chance we're related.
Here's a link to those two places on Wikipedia, and I'm going to use a hyperlink as well as a regular link in the text:
Frostburg : http://en.wikipedia.org/wiki/Frostburg,_Maryland
Magnolia : http://en.wikipedia.org/wiki/Magnolia,_West_Virginia
Do you think this will help with the cousins?
Lists of names and dates... ?
I see Randy Seaver's beautiful blog, Genea-Musings and his posts, Surname Saturday. Here's a sample: http://www.geneamusings.com/2012/12/surname-saturday-clark-england-to.html
Now, my first take on it is that it looks like a whole lotta work! And the second thought is that it looks like perfect cousin bait;) Maybe I need more of that, useful to myself and others and cousin bait crafted to attract.
Trees online.
I do get a couple of messages a week in response to Mom's Big Tree on Ancestry. But that has nothing to do with the blog and I'm wondering if it need be connected in some way or other or does it matter? Maybe they are two different vehicles entirely.
Prominent contact information at the top of the blog.
So if I catch a cousin on a search then it needs to be easy for them to contact me, right? I'm gonna go check my blog right now.... Yup, it's there, but I could repeat it a couple of more times in the page layout so it's easier to find.
OK, that's all I can think of. You have any ideas? I just love it when cousins email!
Photo of the day from the Archive:
Look at the size of that rainbow trout!

subset: commoncrawl


text:
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text:
Professor, Department of Epidemiology & Population Health
Harold and Muriel Block Chair in Epidemiology & Population Health
Division Head, Division of Biostatistics, Department of Epidemiology & Population Health
Director, Biostatistics, Epidemiology and Research Design Core of the Institute for Clinical and Translational Research
Director, Quantitative Sciences in Biomedical Research Center
Dr. Kim's research focuses on statistical methods for designing and analyzing clinical trials and epidemiologic studies (research of the factors affecting health and illness). Most clinical trials aim to demonstrate superiority of an experimental treatment relative to a standard treatment or placebo. An increasing number of trials, however, seek to show that the effects of two treatments on a particular outcome are equivalent, or that one treatment is not inferior to another. These goals are of interest when the new therapy offers benefits such as reduced cost, toxicity, or invasiveness. Dr. Kim is investigating the effects of non-compliance, outcome misclassification and measurement error on equivalence and non-inferiority trials and developing new approaches for defining the non-inferiority margin. Her research interests also include the development of methods for analyzing multivariate and interval-censored survival data. In addition, she collaborates on studies in cancer, rheumatology, and HIV/AIDS.
Dr. Kim has been head of the Division of Biostatistics since 2003, is Director of the Biostatistics Shared Resource of the Institute of Clinical and Translational Research,and Director of the Center for Quantitative Sciences. She is a Fellow of the American Statistical Association, on the American Statistical Association Council of Chapters Governing Board, and is a member of the Medical and Scientific Advisory Council of the Lupus Foundation of America. She has participated on numerous grant review panels for the National Institutes of Health and is currently a member of the NIH Arthritis, Musculoskeletal and Skin Diseases Clinical Trials Review Committee.
Petri, M., Kim, M., Kalunian, K., et al. Combined oral contraceptives are not associated with an increased rate of flare in SLE patients in the SELENA Trial. New England Journal of Medicine 2005; 353:2550 - 2558.
Ginzler, E., Dooley, M., Aranow, C., Kim, M., et al. Comparison of mycophenolate mofetil to intravenous cyclophosphamide for induction therapy of severe active lupus nephritis. New England Journal of Medicine 2005; 353: 2219 - 2228.
Buyon, J., Petri, M., Kim, M., et al. . Estrogen/Cyclic progesterone replacement is associated with an increased rate of mild/moderate but not severe flares in SLE: The SELENA Trial. Annals of Internal Medicine 2005; 953 - 962.
Sheng, D. and Kim, M.Y. The effects of non-compliance on intent-to-treat analysis of equivalence trials. Statistics in Medicine 2006; 25:1183-1199.
Kim, M.Y., Xue, X., Du, E. Approaches for calculating power for case-cohort studies. Biometrics 2006; 62: 929-922.
Xue, X., Kim, M., and Shore, R. Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure. Lifetime Data Analysis 2007; 13:333-350.
Kabat, G., Kim, M., Hunt, S., Chlebowski, R., Rohan, T. Body mass index and waist circumference in relation to lung cancer risk in the Women's Health Initiative. American Journal of Epidemiology 2008; 168:158-169.
Kim, M.Y. Using the instrumental variables estimator to analyze non-inferiority trials with non-compliance. Journal of Biopharmaceutical Statistics 2010; 20:745-758.
Heo, M., Kim, Y., Xue, X., Kim, M. Sample size requirement to detect an intervention effect at the end of follow-up in a longitudinal cluster randomized trial. Statistics in Medicine 2010; 29:382-290.
Izmirly, P., Chalumeau, N., Pisoni, C., Khamashta, M., Kim, M., Saxena, A., Friedman, D., Llanos, C., Piette, J., Buyon, J., Maternal use of hydroxychloroquine is associated with a reduced risk of recurrent anti-SSA/Ro-associated cardiac manifestations of neonatal lupus. Circulation 2012; 126:76-82
Xie, X., Xue, X., Gange, S., Strickler, H., Kim, M., and the WIHS HPV Study Group. Estimationand inference on correlations between biomarkers with repeated measures and left-censoring dueto minimum detection levels. Statistics in Medicine 2012; 31:2275-2289
Xue, X., Kim, M., Gaudet, M., Park, Y., Heo, M., Hollenbeck, A., Strickler, H., Gunter, M. A Comparison of the Polytomous Logistic Regression and Joint Cox Proportional Hazards Models for Evaluating Multiple Disease Subtypes in Prospective Cohort Studies. Cancer Epidemiology, Biomarkers, and Prevention 2013; 22:275-285.
Wang, C., Hall, C., Kim, M. A comparison of power analysis methods for evaluating the effects of a predictor on slopes in longitudinal designs with missing data. Statistical Methods in Medical Research (in press).
More Information About Dr. Mimi Kim
Material in this section is provided by individual faculty members who are solely responsible for its accuracy and content.


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CHLOE GEORGIA BUM POUCHCategories:Other Brands
The Chloe Georgia Bum Pouch is a new bag released not long ago. We've previously featured about the Georgia bag but this time around, Chloe came out with a new design. In fact, the Georgia Bum Pouch is not really a bag because apparently it has no shoulder straps, instead, this Georgia version is a belt bag.
For those who are guilty of shopping till they drop whenever they are traveling abroad especially in budget-friendly countries such as Thailand, Cambodia, Vietnam, and Malaysia, a belt bag is always a must! Practice your haggling skills and wear this structured buffed leather travel pouch from Chloe and you'll be good to go.
Comparing it with other travel belt bags, this bum pouch from Chloe is luxurious and elegant especially with its curb chain belt strap with lanyard clasp fastening. And oh, we also love the beautifully quilted design in black and golden hardware.
The tiered fold over flaps with bumper stud and magnetic press-stud fastening also won our hearts! It measures 6.75' x 5' x 2.25' (L x H x W) inches and is priced €750 euro or $1090 USD via SSENSE.
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A CLOSER LOOK: CHLOE GEORGIA SHOULDER BAGCategories:Other Brands
The Chloe Georgia Shoulder Bag is a stellar piece that's bound to give you one of the best fashion experiences you can ever have…guaranteed! Because of its double-flap storage (which also does well as the bag's unique design element), you'll never have to sacrifice functionality for style. Chloe made it easy for you, babe!
The Georgia comes with a detachable and adjustable shoulder strap which allows you to wear it however you want. It even has twin-flap metallic fastening and a front flap pocket that makes organizing all of your essentials a breeze.
What we're seeing right now are just some of the beautiful takes on the Georgia – a fine calf and lamb leather variation (which comes in a bare neutral) and a metallic gold update. Both pieces are part of the Fall 2015 Collection, which you can check out for yourself.
READ: Chloe Fall 2015 Collection
Prices could range from $1,090 USD to $1,290 USD. What are you waiting for? Get yours now!
Tags: bag Chloe Shoulder GEORGIA
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CHLOE MINI GEORGIA BAGCategories:Other Brands
Today… everything changes more quickly than in the past. That is also true in fashion. To keep up with the demand and 'quick changes', the fashion brand has to adapt with the same speed (or quicker).
That's why companies like ZARA and H&M exists, they can deliver a new accessories and clothes within three weeks, from design to store.
And Chloe surprises us every single time, they successfully introduces new handbags that exceed our expectation or something different that we've have never seen before, yet gorgeous enough to carry as an everyday bag.
The new bag for this season is the Chloe Georgia Bag in mini version. It's the new love for the pre-fall 2015 collection, mixed with calf leather and lambskin and in the colors like intoxicating blue hues.
The most creative part of the Mini Georgia Bag is the double flap – besides decoration purposes, it has also a functional purpose, the double flap allows you to organize your bits and bobs with ease.
And it comes with a long shoulder strap so you can carry it across the body or cross body.
Measuring 6.5' x 9' x 2' inches (H x W x D), priced at $1090 USD via MyTheresa.


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Right, my first ever blog hop! I had no idea such a thing existed until I was tagged by the lovely Bernie McGill who I've mentioned before. Basically it's a chance for writers to share a little bit about their work with other writers and the rest of the world. So, here's everything you wanted to know...
What are you working on at the moment?
The sequel to Death & Co. I have a working title but I'm always madly secretive about my titles in a superstitious sort of way! So for now it is simply known as Book 2!
Where did the idea come from for the book?
Obviously as a sequel the idea came with the first book. It actually came about when I was typing an email to my friend and writing mentor Bernie McGill (who tagged me to do this blog!). I was supposed to be meeting her for coffee and emailed to warn her that I had a stinking cold. Given to hyperbole I started writing, 'I don't just feel like death; I feel like Death's ailing granny...' and felt that sudden PING! that every writer will recognise – that feeling that there's an idea there that just might lead to something. I started writing Death & Co. that night and never looked back.
What genre does your book fall under?
Near world Teen / Crossover fantasy. It's our world with a twist!
Which actors would you choose to play in a film rendition of your book?
Ha! Every writer's fantasy question! Unfortunately it would require a time machine as I have a few ideas for actors but they would all need to be about ten years younger than their current age. So for example Nathanial would be perfectly played by a slightly younger Colin Firth. He has the right combination of compassion and slight careworn-ness – not to mention being so very English! Kristin Scott Thomas would make a lovely Elise – and no one could do Auntie Jo more justice than Jo Brand.
What is the one sentence synopsis for your book?
Gah! Every writer's most hated question! I guess it would be: Fifteen year old Adam is a reluctant Luman, guiding souls into the afterlife – when all he really wants is to be normal. Most of the tension and humour in the book revolves around this.
Will your book be self-published or represented by an agency?
I have a lovely agent called Gillie Russell at Aitken Alexander Associates. She negotiated my deal with Hot Key Books, a fantastic new publisher specialising in teen fiction. They only launched last year but they've already had amazing success – one of their first books Maggot Moon won the Costa Children's Book Prize. So basically my book has found a good home!
How long did it take you to write the first draft of the manuscript?
I wrote it in bits and pieces – so worked hard for a couple of months, then took a four month break, then finally found the motivation to finish it over another couple of months.
What other books would you compare this to within this genre?
This is a really tricky question because I can't think of any other books quite like it. In my mind it's the book lovechild of The Inbetweeners and The Adams Family!
Who or what inspired you to write this book?
I've already described the lightbulb moment up above – but I guess I have always been interested in the supernatural and general existential questions about why we're here, what our lives mean – and of course if there's anything out there after we die. I don't know anyone who hasn't wrestled with these questions at some stage in their lives – and the variety of answers is amazing. Plus I've worked with teenagers for most of my career and they are great craic - so it seems natural to me to make them the heroes of everything I write!
What else about the book might pique the reader's interest?
The book is quite dark in places – but it's also very funny – a bit like life itself. The general consensus from the people who have read it is that ultimately it's a joyful, life-affirming book in spite of the tough subject matter. I'll let you be the judge!
FINITO! That was quick! Now, time to pass the candle onwards and upwards. Having mangled as many metaphors as possible I now present Deirdre Sullivan and KJ Foster. They'll both be posting on the 6th February. Enjoy!


text: Inspired by the natural beauty of Puerto Rico, designer Venessa Arizaga started to hand-make jewellery from seashells, natural stones and a mix of her own charms. In 2010, she debuted her full collection which was based on her exotic trip to Latin America. Featuring a range of creative, statement pieces, Venessa Arizaga necklaces offer versatile designs to complete any look. Her intricate styling combines opulent materials, playful charms and bright threads to create standout pieces of jewellery.


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thanks for your help on trying to download the torrent but i have tried everything but to no avail all im getting is downloading but it dos`nt move just stays on 00
Fender Fingers, Thanks for making it real. Thought the ticket and handbill would post. Found two other articles from the Albuquerque Journal 09/19/1999 and the Rio Rancho Observer 12/23/2012. Both mention two shows on 06/19/1970. Could find no review. Would like to talk to someone else who attended if you know anyone.
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History: A 6-year-old female spayed Lionhead rabbit presented to Tufts Foster Hospital for Small Animals with a one-day history of inappetance. She also had a recent history of urine scald. On physical examination the rabbit was noted to be overweight with increased subcutaneous and abdominal fat stores. The results of a complete blood count revealed a leukocytosis (26.2 x 103cells/µl; reference range, 5 – 12 x 103cells/µl) with a lymphocytosis (23.4 x 103cells/µl; reference range, 1.25 – 6 x 103cells/µl). Continue reading
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Thymoma in Rabbits
Rabbits normally have a large thymus that is placed cranial to the heart and extends into the thoracic inlet. Thymoma, thymic lymphoma and thymic carcinoma have all been reported in rabbits. Thymic lymphoma and carcinoma are rare in rabbits.The overall incidence of thymoma in rabbits is low (around 7% of reported neoplasms in 55 colony rabbits) with no apparent sex predilection. The mean age at presentation is 6.7 years based on one survey of 19 cases. Rabbit thymomas are generally slow growing and are potentially locally invasive tumors that rarely metastasize. Continue reading


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Dynacirc Capsule
GENERIC NAME(S): Isradipine
OTHER NAME(S): Dynacirc Capsule
Isradipine is used with or without other medications to treat high blood pressure (hypertension). Lowering high blood pressure helps prevent strokes, heart attacks, and kidney problems. Isradipine is called a calcium channel blocker. It works by relaxing blood vessels so blood can flow more easily.
How to use Dynacirc Capsule
Take this medication by mouth, usually twice daily with or without food or as directed by your doctor.
The dosage is based on your medical condition and response to treatment. Your doctor may gradually increase your dose. Follow your doctor's instructions carefully.
Use this medication regularly to get the most benefit from it. To help you remember, take it at the same times each day. It is important to continue taking this medication even if you feel well. Most people with high blood pressure do not feel sick. It may take 2 to 4 weeks before you get the full benefit of this drug.
Tell your doctor if your condition does not improve or if it worsens (for example, your blood pressure readings remain high or increase).
Side Effects
Dizziness, nausea, headache, tiredness, flushing, and swelling of the ankles/feet may occur. If any of these effects persist or worsen, tell your doctor or pharmacist promptly.
To reduce dizziness and lightheadedness, get up slowly when rising from a sitting or lying position.
Remember that your doctor has prescribed this medication because he or she has judged that the benefit to you is greater than the risk of side effects. Many people using this medication do not have serious side effects.
Tell your doctor right away if any of these unlikely but serious side effects occur: fainting, fast/irregular/pounding heartbeat.
Tell your doctor right away if any of these rare but very serious side effects occur: vision changes.
Some people who already have severe heart disease may rarely develop worsening chest pain or a heart attack after starting this medication or increasing the dose. Get medical help right away if you experience: worsening chest pain, symptoms of a heart attack (such as chest/jaw/left arm pain, shortness of breath, unusual sweating).
A very serious allergic reaction to this drug is rare. However, seek immediate medical attention if you notice any symptoms of a serious allergic reaction, including: rash, itching/swelling (especially of the face/tongue/throat), severe dizziness, trouble breathing.
This is not a complete list of possible side effects. If you notice other effects not listed above, contact your doctor or pharmacist.
In the US -
Call your doctor for medical advice about side effects. You may report side effects to FDA at 1-800-FDA-1088 or at www.fda.gov/medwatch.
In Canada - Call your doctor for medical advice about side effects. You may report side effects to Health Canada at 1-866-234-2345.
Before taking isradipine, tell your doctor or pharmacist if you are allergic to it; or if you have any other allergies. This product may contain inactive ingredients, which can cause allergic reactions or other problems. Talk to your pharmacist for more details.
This medication should not be used if you have certain medical conditions. Before using this medicine, consult your doctor or pharmacist if you have: certain structural heart problem (aortic stenosis).
Before using this medication, tell your doctor or pharmacist your medical history, especially of: liver disease, kidney disease.
This drug may make you dizzy. Do not drive, use machinery, or do any activity that requires alertness until you are sure you can perform such activities safely. Limit alcoholic beverages.
Before having surgery, tell your doctor or dentist that you are taking this medication.
During pregnancy, this medication should be used only when clearly needed. Discuss the risks and benefits with your doctor.
It is not known if this medication passes into breast milk. Consult your doctor before breast-feeding.
Your doctor or pharmacist may already be aware of any possible drug interactions and may be monitoring you for them. Do not start, stop, or change the dosage of any medicine before checking with your doctor or pharmacist first.
Before using this medication, tell your doctor or pharmacist of all prescription and nonprescription/herbal products you may use.
Other medications can affect the removal of isradipine from your body, which may affect how isradipine works. Examples include cimetidine, azole antifungals (such as itraconazole), macrolide antibiotics (such as erythromycin), rifamycins (such as rifabutin), St. John's wort, drugs used to treat seizures (such as phenytoin), among others.
Cimetidine is a nonprescription drug that is commonly used to treat extra stomach acid. Because cimetidine may interact with isradipine, ask your pharmacist about other products to treat stomach acid.
Some products have ingredients that could raise your blood pressure. Tell your pharmacist what products you are using, and ask how to use them safely (especially cough-and-cold products, diet aids, or NSAIDs such as ibuprofen/naproxen).
This document does not contain all possible interactions. Therefore, before using this product, tell your doctor or pharmacist of all the products you use. Keep a list of all your medications with you, and share the list with your doctor and pharmacist.
If someone has overdosed and has serious symptoms such as passing out or trouble breathing, call 911. Otherwise, call a poison control center right away. US residents can call their local poison control center at 1-800-222-1222. Canada residents can call a provincial poison control center.
Do not share this medication with others. Talk with your doctor about making changes to your lifestyle that may help this medication work better (such as stress reduction programs, exercise, and dietary changes).
Laboratory and/or medical tests (such as liver function tests, blood pressure, electrocardiograms) may be performed from time to time to monitor your progress or check for side effects. Consult your doctor for more details.
There are different types of this medication available. They may not have the same effects. Do not change types without consulting your doctor or pharmacist.
Have your blood pressure checked regularly while taking this medication. Discuss with your doctor how to monitor your own blood pressure.
Missed Dose
If you miss a dose, take it as soon as you remember. If it is near the time of the next dose, skip themissed dose and resume your usual dosing schedule. Do not double the dose to catch up.
Store at room temperature between 68-77 degrees F (20-25 degrees C) away from light and moisture. Do not store in the bathroom. Keep all medicines away from children and pets.
Do not flush medications down the toilet or pour them into a drain unless instructed to do so. Properly discard this product when it is expired or no longer needed. Consult your pharmacist or local waste disposal company for more details about how to safely discard your product.Information last revised June 2017. Copyright(c) 2017 First Databank, Inc.
Sorry. No images are available for this medication.
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