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Performance Analysis of Classification of Data using Structured Induction Decision Tree Algorithms

Decision Trees are one of the most popular Data Mining models. Decision Trees are able to provide a set of rules which improves the user's understanding. Decision Trees represent variables and variable values as trees, branches and leaves from which Decision Rules must be transformed. A Decision Tree classifier builds a model by recursively dividing the training set into partitions so that all or most of the records in a partition have the same class label. Most Decision Tree classifiers perform classification in two phases: tree-induction and tree-pruning. In the tree-induction phase the algorithm starts with the whole data set at the root node. The data set is partitioned according to a splitting criterion into subsets. This procedure is repeated recursively for each subset until each subset contains only members belonging to the same class or is sufficiently small. In the treepruning phase the full grown tree is cut back to prevent over-fitting and to improve the accuracy of the tree. The most time-consuming part of Decision Tree induction is obviously the choice of the best attribute selection measure. In this paper we propose decision tree induction algorithm for categorizing the data. We observe from the experimental results that our techniques work well on high dimensional categorical data in which attributes contain distinct values less than ten. For large quantities of categorical values, discretization technique is necessary. In this paper we describe the frequently used decision tree algorithms ID3, C4.5, CART, machine learning concepts and importance of ontologies in classification concepts.