CONFIDENCE METRICS FOR ASSOCIATION RULE MINING

Abstract

We propose a simple, novel, and yet effective confidence metric for measuring the interestingness of association rules. Distinguishing from existing confidence measures, our metrics really indicate the positively companionate correlations between frequent itemsets. Furthermore, some desired properties are derived for examining the goodness of confidence measures in terms of probabilistic significance. We systematically analyze our metrics and traditional ones, and demonstrate that our new algorithm significantly captures the mainstream properties. Our approach will be useful to many association analysis tasks where one must provide actionable association rules and assist users to make quality decisions.

This work was supported in part by the Australian Research Council (ARC) under large grant DP0985456, the Nature Science Foundation (NSF) of China under grant 90718020 the China 973 Program under grant 2008CB317108, the Research Program of China Ministry of Personnel for Overseas-Return High-Level Talents, the MOE Project of Key Research Institute of Humanities and Social Sciences at Universities (07JJD720044), and the Guangxi NSF (Key) grants.