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Mining Top-K Non-redundant Association Rules

  • Philippe Fournier-Viger
  • Vincent S. Tseng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7661)

Abstract

Association rule mining is a fundamental data mining task. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information.Furthermore, it is well-known that a large proportion of association rules generated are redundant. In previous works, these two problems have been addressed separately. In this paper, we address both of them at the same time by proposing an approximate algorithm named TNR for mining top-k non redundant association rules.

Keywords

association rules top-k non-redundant rules algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
  • Vincent S. Tseng
    • 2
  1. 1.Dept. of Computer ScienceUniversity of MonctonCanada
  2. 2.Dept. of Computer Science and Info. EngineeringNational Cheng Kung UniversityTaiwan

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