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Mining Optimal Class Association Rule Set

  • Jiuyong Li
  • Hong Shen
  • Rodney Topor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2035)

Abstract

We define an optimal class association rule set to be the minimum rule set with the same prediction power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining the optimal class association rule set using an upward closure property of pruning weak rules before they are actually generated. We have implemented the algorithm and our experimental results show that our algorithm generates the optimal class association rule set, whose size is smaller than 1/17 of the complete class association rule set on average, in significantly less time than generating the complete class association rule set. Our proposed criterion has been shown very effective for pruning weak rules in dense databases.

Keywords

Association Rule Minimum Support Association Rule Mining Prediction Power Strong Rule 
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 2001

Authors and Affiliations

  • Jiuyong Li
    • 1
  • Hong Shen
    • 1
  • Rodney Topor
    • 1
  1. 1.School of Computing and Information TechnologyGriffith UniversityNathanAustralia

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