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A Global Constraint for Closed Frequent Pattern Mining

  • Nadjib Lazaar
  • Yahia Lebbah
  • Samir Loudni
  • Mehdi Maamar
  • Valentin Lemière
  • Christian Bessiere
  • Patrice Boizumault
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9892)

Abstract

Discovering the set of closed frequent patterns is one of the fundamental problems in Data Mining. Recent Constraint Programming (CP) approaches for declarative itemset mining have proven their usefulness and flexibility. But the wide use of reified constraints in current CP approaches leads to difficulties in coping with high dimensional datasets. In this paper, we propose the ClosedPattern global constraint to capture the closed frequent pattern mining problem without requiring reified constraints or extra variables. We present an algorithm to enforce domain consistency on ClosedPattern in polynomial time. The computational properties of this algorithm are analyzed and its practical effectiveness is experimentally evaluated.

Keywords

Frequent Pattern Constraint Programming Paradigm Pattern Mining Constraint Satisfaction Problem Global Constraint 
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 International Publishing Switzerland 2016

Authors and Affiliations

  • Nadjib Lazaar
    • 1
  • Yahia Lebbah
    • 2
  • Samir Loudni
    • 3
  • Mehdi Maamar
    • 1
    • 2
  • Valentin Lemière
    • 3
  • Christian Bessiere
    • 1
  • Patrice Boizumault
    • 3
  1. 1.LIRMM, University of MontpellierMontpellierFrance
  2. 2.LITIOUniversity of Oran 1 Ahmed Ben BellaOranAlgeria
  3. 3.GREYC, Normandie UniversityCaenFrance

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