Proposal of New Objective Measures for Mining Association Rules: Cannibalization and Unexpectedness

In view of the problems that a few of the existing measures for association rules does not directly meet user’s requirements, and association mining algorithms produce huge number of trivial rules, this paper proposes two new objective measures for mining association rules to solve the problems. The first measure is the degree of cannibalization between itemsets, which is bounded up with marketing strategy, and the second is the objective measure that intends to discover unexpected rules in the database. Experimental studies with application to public dataset and comparison of running time using synthetic datasets demonstrate the validity and effectiveness of the proposed measures.

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