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Combined Pattern Mining: From Learned Rules to Actionable Knowledge

  • Yanchang Zhao
  • Huaifeng Zhang
  • Longbing Cao
  • Chengqi Zhang
  • Hans Bohlscheid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5360)

Abstract

Association mining often produces large collections of association rules that are difficult to understand and put into action. In this paper, we have designed a novel notion of combined patterns to extract useful and actionable knowledge from a large amount of learned rules. We also present definitions of combined patterns, design novel metrics to measure their interestingness and analyze the redundancy in combined patterns. Experimental results on real-life social security data demonstrate the effectiveness and potential of the proposed approach in extracting actionable knowledge from complex data.

Keywords

Association Rule Learned Rule Association Rule Mining Actionable Knowledge Customer Group 
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 2008

Authors and Affiliations

  • Yanchang Zhao
    • 1
  • Huaifeng Zhang
    • 1
  • Longbing Cao
    • 1
  • Chengqi Zhang
    • 1
  • Hans Bohlscheid
    • 2
  1. 1.Data Sciences & Knowledge Discovery Research Lab Centre for Quantum Computation and Intelligent Systems Faculty of Engineering & ITUniversity of TechnologySydneyAustralia
  2. 2.Projects Section, Business Integrity Programs BranchCentrelinkAustralia

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