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Mining potentially more interesting association rules with fuzzy interest measure
Authors:Wei-Min Ma  Ke Wang  Zhu-Ping Liu
Affiliation:(1) School of Economics and Management, Tongji University, Shanghai, 200092, China;(2) School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing, 100083, China
Abstract:The association rules, discovered by traditional support–confidence based algorithms, provide us with concise statements of potentially useful information hidden in databases. However, only considering the constraints of minimum support and minimum confidence is far from satisfying in many cases. In this paper, we propose a fuzzy method to formulate how interesting an association rule may be. It is indicated by the membership values belonging to two fuzzy sets (i.e., the stronger rule set and the weaker rule set), and thus provides much more flexibility than traditional methods to discover some potentially more interesting association rules. Furthermore, revised algorithms based on Apriori algorithm and matrix structure are designed under this framework.
Keywords:
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