Improved decision tree induction: Prioritized Height Balanced tree with entropy to find hidden rules

Authors:
Mohd Mahmood Ali;M. S. Qaseem;Lakshmi Rajamani;A. Govardhan
Affiliations:
Muffakamjah College of Engg. & Technology, Hyderabad, Andhra Pradesh, India;Nizam Institute of Engg. & Technology, Nalgonda, Andhra Pradesh, India;Osmania University, Hyderabad, Andhra Pradesh, India;Jawaharlal Nehru Technological University (H), India
Venue:
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Year:
2012

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Abstract

Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. We suggest improvements to the existing C4.5 decision tree algorithm. In this paper attribute oriented induction (AOI) and relevance analysis are incorporated with concept hierarchy's knowledge and HeightBalancePriority algorithm for construction of decision tree along with Multi level mining. The assignment of priorities to attributes is done by evaluating information entropy, at different levels of abstraction for building decision tree using HeightBalancePriority algorithm. Modified DMQL queries are used to understand and explore the shortcomings of the decision trees generated by C4.5 classifier for education dataset and the results are compared with the proposed approach.