Decision Tree Induction: Data Classification using Height-Balanced Tree

Classification is considered as one of the building blocks in data mining problem and the major issues concerning data mining in large databases are efficiency and scalability. In this paper we propose a data classification method using AVL trees enhances the quality and stability of data mining problems. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of growing a decision tree from 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 that performance evaluation of the proposed algorithm that uses the algorithm to acquire designing rule from the knowledge database is discussed in the paper.