COMPARATIVE STUDY OF DECISION TREE ALGORITHMS FOR DATA ANALYSIS

The Main objective of this paper is to compare the classification algorithms for decision trees for data analysis. Classification problem is important task in data mining. Because today’s databases are rich with hidden information that can be used for making intelligent business decisions. To comprehend that information, classification is a form of data analysis that can be used to extract models describing important data classes or to predict future data trends. Several classification techniques have been proposed over the years e.g., neural networks, genetic algorithms, Naive Bayesian approach, decision trees, nearest-neighbor method etc. In this paper, our attention is restricted to decision tree technique after considering all its advantages compared to other techniques. There exist a large number of algorithms for inducing decision trees like CHAID, FACT, C4.5, CART etc. But in this paper, these five decision tree classification algorithms are considered ? ID3, SLIQ, SPRINT, PUBLIC and RAINFOREST.