Abstract—Classification is considered to be one of the important building blocks in data mining problem. The major issues concerning data mining in large databases are efficiency and scalability. This paper addresses these issues by proposing a data classification method using AVL trees, which enhances the quality and stability. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of building a decision tree from the available data. Specifically, we consider a scenario in which we apply the multi level mining method on the data set and show how the proposed approach tend to give the efficient multiple level classifications of large amounts of data. The results specify the improved performance of the proposed algorithm which acquires designing rules from the knowledge database.

Index Terms—Decision Tree Induction, Generalization, Data Classification, Multi Level mining, Balanced Decision Tree Construction.

¹ Devi Prasad Bhukya is with the Osmania University, Hyderabad, Andhra Pradesh, India; phone: +91-4027097577; fax: +91 (40) 27095179; e-mail: deviprasad@osmania.ac.in.
² Prof. S Ramachandram. is with the Osmania University, Hyderabad, Andhra Pradesh, India, phone: +91-4027097577; fax: +91 (40) 27095179; e-mail: schandram@osmania.ac.in.

Cite: Devi Prasad Bhukya and S. Ramachandram, "Decision Tree Induction: An Approach for Data Classification Using AVL-Tree," International Journal of Computer and Electrical Engineering vol. 2, no. 4, pp. 660-665, 2010.