Efficient Rule-Based Attribute-Oriented Induction for Data Mining

Authors: Cheung D.W.¹; Hwang H.Y.²; Fu A.W.³; Han J.⁴

Source: Journal of Intelligent Information Systems, Volume 15, Number 2, 10 September 2000 , pp. 175-200(26)

Publisher: Springer

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Abstract:

Data mining has become an important technique which has tremendous potential in many commercial and industrial applications. Attribute-oriented induction is a powerful mining technique and has been successfully implemented in the data mining system DBMiner (Han et al. Proc. 1996 Int'l Conf. on Data Mining and Knowledge Discovery (KDD'96), Portland, Oregon, 1996). However, its induction capability is limited by the unconditional concept generalization. In this paper, we extend the concept generalization to rule-based concept hierarchy, which enhances greatly its induction power. When previously proposed induction algorithm is applied to the more general rule-based case, a problem of induction anomaly occurs which impacts its efficiency. We have developed an efficient algorithm to facilitate induction on the rule-based case which can avoid the anomaly. Performance studies have shown that the algorithm is superior than a previously proposed algorithm based on backtracking.

Keywords: data mining; knowledge discovery in databases; rule-based concept generalization; rule-based concept hierarchy; attribute-oriented induction; inductive learning; learning and adaptive systems

Language: English

Document Type: Regular paper

Affiliations: 1: Department of Computer Science and Information Systems, The University of Hong Kong, Hong Kong. dcheung@csis.hku.hk 2: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong 3: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong. adafu@cse.cuhk.edu.hk 4: School of Computing Science, Simon Fraser University, Canada. han@cs.sfu.ca

Publication date: 2000-09-10