Discovering Interesting Association Rules by Clustering

  • Yanchang Zhao
  • Chengqi Zhang
  • Shichao Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3339)

Abstract

There are a great many metrics available for measuring the interestingness of rules. In this paper, we design a distinct approach for identifying association rules that maximizes the interestingness in an applied context. More specifically, the interestingness of association rules is defined as the dissimilarity between corresponding clusters. In addition, the interestingness assists in filtering out those rules that may be uninteresting in applications. Experiments show the effectiveness of our algorithm.

Keywords

Interestingness Association Rules Clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yanchang Zhao
    • 1
  • Chengqi Zhang
    • 1
  • Shichao Zhang
    • 1
  1. 1.Faculty of Information TechnologyUniv. of TechnologySydneyAustralia

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