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The rapid explosion in retail data calls for more effective and efficientdiscovery of association rules to develop relevant business strategies andrules.Unlike online shopping sites, most brick and mortar retail shops arelocated in geographically and demographically diverse areas. This diversitypresents a new challenge to the classical association rule model which assumesa homogenous group of customers behaving differently. The focus of this paperis centered on the discovery of association rules that were hidden as a resultof a geographical and demographically diverse data. We will introduce a novelmeasure which incorporates the entropy measure with modified weighting for thedetection of association rules not detected by the standard measures due toSimpson's paradox. The proposed measure is evaluated using a real-word casestudy involving a major retailer of fashion good in the context of traditionalbrick and mortar setting.

Modified Entropy Measure for Detection of Association Rules Under Simpson's Paradox Context