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Parallel Branch-and-Bound Graph Search for Correlated Association Rules

  • Shinichi Morishita
  • Akihiro Nakaya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1759)

Abstract

There have been proposed efficient ways of enumerating all the association rules that are interesting with respect to support, confidence, or other measures. In contrast, we examine the optimization problem of computing the optimal association rule that maximizes the significance of the correlation between the assumption and the conclusion of the rule. We propose a parallel branch-and-bound graph search algorithm tailored to this problem. The key features of the design are (1) novel branch-and-bound heuristics, and (2) a rule of rewriting conjunctions that avoids maintaining the list of visited nodes. Experiments on two different types of large-scale shared-memory multi-processors confirm that the speed-up of the computation time scales almost linearly with the number of processors, and the size of search space could be dramatically reduced by the branch-and-bound heuristics.

Keywords

Execution Time Association Rule Search Tree Mining Association Rule Apriori Algorithm 
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 2000

Authors and Affiliations

  • Shinichi Morishita
    • 1
  • Akihiro Nakaya
    • 2
  1. 1.Department of Information Science Office 301, 7th Building Faculty of ScienceUniversity of TokyoTokyoJapan
  2. 2.Department of Genome Knowledge Discovery System (Hitachi) Institute of Medical ScienceUniversity of TokyoTokyoJapan

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