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Evaluating Association Rules by Quantitative Pairwise Property Comparisons

Evaluating association rules is an integral post process in association rule mining. Association rules are examined by measures for their interestingness. Different interestingness measures have been proposed. Given an association rule mining task, measures are assessed and selected against a set of user-specified properties. However, in practice, due to the subjectivity and imperfection in property specifications, it is a non-trivial task to make appropriate measure selection. In this work, we propose a novel measure selection approach that makes use of the Analytic Hierarchy Process (AHP), a scheme for making complex decisions. Our approach captures a user¡¯s desired requirements quantitatively in an application domain to assess interestingness measures. It detects inconsistencies in property specifications, and is invariant to the number of association rules to be evaluated. The effectiveness of our approach is shown through case studies.