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Modified Entropy Measure for Detection of Association Rules Under Simpson's Paradox Context | | Murphy Choy
; Cally Claire Ong
; Michelle Cheong
; | | Date: |
4 Oct 2012 | | Abstract: | The rapid explosion in retail data calls for more effective and efficient
discovery of association rules to develop relevant business strategies and
rules.Unlike online shopping sites, most brick and mortar retail shops are
located in geographically and demographically diverse areas. This diversity
presents a new challenge to the classical association rule model which assumes
a homogenous group of customers behaving differently. The focus of this paper
is centered on the discovery of association rules that were hidden as a result
of a geographical and demographically diverse data. We will introduce a novel
measure which incorporates the entropy measure with modified weighting for the
detection of association rules not detected by the standard measures due to
Simpson’s paradox. The proposed measure is evaluated using a real-word case
study involving a major retailer of fashion good in the context of traditional
brick and mortar setting. | | Source: | arXiv, 1210.1288 | | Services: | Forum | Review | PDF | Favorites | |
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