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Finding the Most Interesting Association Rules by Aggregating Objective Interestingness Measures

  • Tri Thanh Nguyen Le
  • Hiep Xuan Huynh
  • Fabrice Guillet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5465)

Abstract

Association rule post-processing is a research challenge in KDD. In this post-processing task, objective interestingness measures are very useful for finding interesting rules possessing certain characteristics. Till now, the usual method for using objective interestingness measures is to select one or several suitable measures for filtering rules. This paper proposes a new approach to aggregate a set of interestingness measures using the Choquet integral as an advanced aggregation operator. Since an objective interestingness measure is considered as a point of view on rule quality, the aggregation of a set of objective interestingness measures can extract rules satisfying many points of view. The experiment is carried out on different groups (i.e. different natures) of objective interestingness measures to observe their behaviors.

Keywords

Mutual Information Association Rule Gini Index Mining Association Rule Aggregation Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tri Thanh Nguyen Le
    • 1
  • Hiep Xuan Huynh
    • 1
  • Fabrice Guillet
    • 2
  1. 1.College of Information and Communication TechnologyCan Tho UniversityVietnam
  2. 2.Polytechnics School of Nantes UniversityFrance

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