Efficient mining of both positive and negative association rules

Authors:
Xindong Wu;Chengqi Zhang;Shichao Zhang
Affiliations:
University of Vermont, Burlington, Vermont;University of Technology, Sydney, Australia;University of Technology, Sydney, Australia and Tsinghua University, China
Venue:
ACM Transactions on Information Systems (TOIS)
Year:
2004

Citing 17
Cited 66

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Abstract

This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A ⇒ ¬ B, ¬ A ⇒ B, and ¬ A ⇒ ¬ B, which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its effectiveness and efficiency.