Analysis of Various Decision Tree Algorithms for Classification in Data Mining

Today the computer technology and computer network technology has developed so much and is still developing with pace.Thus, the amount of data in the information industry is getting higher day by day. This large amount of data can be helpful for analyzing and extracting useful knowledge from it. The hidden patterns of data are analyzed and then categorized into useful knowledge. This process is known as Data Mining. [4].Among the various data mining techniques, Decision Tree is also the popular one. Decision tree uses divide and conquer technique for the basic learning strategy. A decision tree is a flow chart-like structure in which each internal node represents a “test” on an attribute where each branch represents the outcome of the test and each leaf node represents a class label. This paper discusses various algorithms of the decision tree (ID3, C4.5, CART), their features, advantages, and disadvantages.