Confidence Metrics For Association Rule Mining

Publisher:
Taylor & Francis Inc
Publication Type:
Journal Article
Citation:
Applied Artificial Intelligence, 2009, 23 (8), pp. 713 - 737
Issue Date:
2009-01
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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.
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