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Evaluating Interestingness Measures with Linear Correlation Graph

  • Xuan-Hiep Huynh
  • Fabrice Guillet
  • Henri Briand
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)

Abstract

Making comparisons from the post-processing of association rules have become a research challenge in data mining. By evaluating interestingness value calculated from interestingness measures on association rules, a new approach based on the Pearson’s correlation coefficient is proposed to answer the question: How we can capture the stable behaviors of interestingness measures on different datasets?. In this paper, a correlation graph is used to evaluate the behavior of 36 interestingness measures on two datasets.

Keywords

Association Rule Mining Association Rule Good Rule Interestingness Measure Correlation Graph 
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 2006

Authors and Affiliations

  • Xuan-Hiep Huynh
    • 1
  • Fabrice Guillet
    • 1
  • Henri Briand
    • 1
  1. 1.LINA CNRS 2729 – Polytechnic School of Nantes UniversityNantesFrance

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