On Segmentation of Interaction Values
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Post-processing of association rules with interestingness measures is considered as one of the most difficult and interesting task in the research domain of Knowledge Discovery from Databases (KDD). In this paper, we propose a new approach to discover the behaviors of interestingness measures by modeling the interaction between them. The interaction values are calculated based on the capacity function (also called fuzzy measure) and then are segmented to discover the interaction’s trends of clusters of interestingness measures.
KeywordsKnowledge Discovery from Databases association rules interestingness measures correlation graph capacity function Sugeno measure interaction value segmentation
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