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Discovering Both Positive and Negative Fuzzy Association Rules in Large Transaction Databases


Jianchao Han, and Mohsen Beheshti


Department of Computer Science, California State University, Dominguez Hills, 1000 E. Victoria St., Carson, CA 90747, USA


Received: February 22, 2005

Accepted: December 21, 2005


Keywords: data mining and knowledge discovery, association rules, positive and negative association rules, fuzzy association rules

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.3 pp. 287-294, 2006

Abstract



Mining association rules is an important task of dara mining and knowledge discovery. Traditional association rules mining is built on transaction databases, which has some limitations. Two of these limitations are 1) each transaction merely contains binary items, meaning that an item either occurs in a transaction or not; 2) only positive association rules are discovered, while negative associations are ignored. Mining fuzzy association rules has been proposed to address the first limitation, while mining algorithms for negative association rules have been developed to resolve the second limitation. In this paper, we combine these two approaches to propose a novel approach for mining both positive and negative fuzzy association rules. The interestingness measure for both positive and negative fuzzy association rule is proposed, the algorithm for mining these rules is described, and an illustrative example is presented to demonstrate how the measure and the algorithm work.
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