Affiliations: [a] School of EEE, Nanyang Technological University, BLK S1, Nanyang Avenue, Singapore 639798. Fax: +65 793 3318; E-mail: [email protected] | [b] Department of Computer Science, School of Computing, National University of Singapore, Lower Kent Ridge Road, Singapore 119260 | [c] Microsoft Research China, Sigma Center, 49 Zhichun Road, Beijing 100080, P.R. China
Abstract: Some existing data mining methods, such as classification trees, neural networks and association rules, have the drawbacks that the user's prior knowledge cannot be easily specified and incorporated into the knowledge discovery process, and the rules mined from databases lack quantitative analyses. In this paper, we propose a belief networks method for rule mining, which takes the advantage of belief networks as the directed acyclic graph language and their function for numerical representation of probabilistic dependencies among the variables in the database, so that it can overcome the drawbacks. Since belief networks provide a natural representation for capturing causal relationship among a set of variables, our proposed method can mine more general correlation rules which can capture the relationship of more than two attribute variables. The potential application of the proposed method is demonstrated through the detailed case studies on benchmark databases.
