Abstract—Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm which is improved version of ID3. In our proposed algorithm attributes are divided into groups and then we apply the selection measure 5 for these groups. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
Index Terms—Classification, decision tree, knowledge engineering, data mining, supervised learning.
Vijendra Singh is with Department of Computer Science and Engineering, Faculty of Engineering and Technology, Mody Institute of Technology and Science, Lakshmangarh, Sikar, Rajasthan, India (email: email@example.com ).
Hem Jyotsana Parashar and Nisha Vasudeva are with Department of Computer Science Engineering , Arya college of Engineering and IT, Kukas , Jaipur Rajasthan, India (email: firstname.lastname@example.org; email@example.com ).
Cite: Hem Jyotsana Parashar, Singh Vijendra, and Nisha Vasudeva, "An Efficient Classification Approach for Data Mining," International Journal of Machine Learning and Computing vol. 2, no. 4, pp. 446-448, 2012.