A Survey on Decision Tree Based Approaches in Data Mining

Decision tree learning is the most popular and powerful approach in knowledge discovery as well as in data mining. This is used for exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. Classification algorithm processes a training set containing a set of attributes. ID3 algorithm is the most widely used decision tree based algorithms. The main disadvantage of the ID3 algorithm is that it chooses the attribute based on occurrence not on the importance. So in this paper we are going to discuss the ID3 based algorithms which select the attribute based on the importance. This paper discusses about the various ID3 based decision tree algorithms such as Improved ID3 based on attribute importance include the Association function, Attribute importance and Attribute weight. Finally compared all the above algorithms based on the working strategies, characteristics, and features. Keywords— Decision Tree learning, Classification, ID3, Improved ID3, Entropy

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