A Data Analysis Approach for Evaluating the Behavior of Interestingness Measures

  • Xuan-Hiep Huynh
  • Fabrice Guillet
  • Henri Briand
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3735)


In recent years, the problem of finding the different aspects existing in a dataset has attracted many authors in the domain of knowledge quality in KDD. The discovery of knowledge in the form of association rules has become an important research. One of the most difficult issues is that an enormous number of association rules are discovered, so it is not easy to choose the best association rules or knowledge for a given dataset. Some methods are proposed for choosing the best rules with an interestingness measure or matching properties of interestingness measure for a given set of interestingness measures. In this paper, we propose a new approach to discover the clusters of interestingness measures existing in a dataset. Our approach is based on the evaluation of the distance computed between interestingness measures. We use two techniques: agglomerative hierarchical clustering (AHC) and partitioning around medoids (PAM) to help the user graphically evaluates the behavior of interestingness measures.


Association Rule Mining Association Rule Agglomerative Hierarchical Cluster Interestingness Measure Data Analysis Approach 
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 2005

Authors and Affiliations

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

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