Mining a complete set of both positive and negative association rules from large databases

Authors:
Hao Wang;Xing Zhang;Guoqing Chen
Affiliations:
Department of Management Science and Engineering, Tsinghua University, Beijing, China;Department of Management Science and Engineering, Tsinghua University, Beijing, China;Department of Management Science and Engineering, Tsinghua University, Beijing, China
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

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Abstract

Association rule mining is one of the key issues in knowledge discovery. In recent years, negative association rule mining has attracted remarkable attention. This paper presents a notion of validity for both positive and negative association rules, which is considered intuitive and necessary. Then, a mining algorithm to find all rules in light of completeness is proposed. In doing so, several pruning strategies based on the upward closure property are developed and incorporated into the algorithm so as to guarantee the computational efficiency.