Efficient Parallel Mining of Gradual Patterns on Multicore Processors

  • Anne LaurentEmail author
  • Benjamin Négrevergne
  • Nicolas Sicard
  • Alexandre Termier
Part of the Studies in Computational Intelligence book series (SCI, volume 398)


Mining gradual patterns plays a crucial role in many real world applications where huge volumes of complex numerical data must be handled, e.g., biological databases, survey databases, data streams or sensor readings. Gradual patterns highlight complex order correlations of the form “The more/less X, the more/less Y”. Only recently algorithms have appeared to mine efficiently gradual rules. However, due to the complexity of mining gradual rules, these algorithms cannot yet scale on huge real world datasets. In this paper, we thus propose to exploit parallelism in order to enhance the performances of the fastest existing one (GRITE) on multicore processors. Through a detailed experimental study, we show that our parallel algorithm scales very well with the number of cores available.


Association Rule Pattern Mining Frequent Itemsets Multicore Processor Test Database 
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 Berlin Heidelberg 2012

Authors and Affiliations

  • Anne Laurent
    • 1
    Email author
  • Benjamin Négrevergne
    • 2
  • Nicolas Sicard
    • 3
  • Alexandre Termier
    • 2
  1. 1.Univ. Montpellier 2, LIRMM, CNRS UMR 5506Montpellier cedex 5France
  2. 2.LIG, UJF, CNRS UMR 5217Saint Martin d’HèresFrance
  3. 3.LRIE, EFREIVillejuifFrance

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