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On Segmentation of Interaction Values

  • Nguyen Chi Lam
  • Hiep Xuan Huynh
  • Fabrice Guillet
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Part of the Studies in Computational Intelligence book series (SCI, volume 363)

Abstract

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.

Keywords

Knowledge Discovery from Databases association rules interestingness measures correlation graph capacity function Sugeno measure interaction value segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nguyen Chi Lam
    • 1
  • Hiep Xuan Huynh
    • 1
  • Fabrice Guillet
    • 2
  1. 1.College of Information and Communication TechnologyCantho UniversityVietnam
  2. 2.Polytechnic School of Nantes UniversityFrance

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