Decision tree induction: Priority classification

Scalability and efficiency is the major problem for classification algorithms in data mining, for large databases. We have suggested improvements to an existing C4.5 decision tree Algorithm. Particularly, when decision tree induction, used to construct the decision tree. In this paper Attribute oriented induction (AOI) and relevance analysis incorporated with concept hierarchy's knowledge and height-balancing tree (AVL tree) for construction of decision tree. MDL cost can be accurately calculated for decision tree considering nodes, at different levels. The other two aspects discussed in this paper is without and with priority given for attributes, at different levels of abstraction for building decision tree using DMQL, along with multilevel mining applied and the results obtained, are compared with J48/C4.5 classifier.