A Global Constraint for Closed Frequent Pattern Mining


Nadjib Lazaar 1 Yahia Lebbah 2 Samir Loudni 3 Mehdi Maamar 2 Valentin Lemière 3, 4 Christian Bessière 1 Patrice Boizumault 3
1 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 LITIO - Laboratoire d'Informatique et Technologies de l'Information d'Oran
3 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
4 GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
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.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01374719
Contributor : Joël Quinqueton <>
Submitted on : Monday, October 15, 2018 - 1:10:50 PM
Last modification on : Thursday, February 7, 2019 - 5:32:13 PM
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Nadjib Lazaar, Yahia Lebbah, Samir Loudni, Mehdi Maamar, Valentin Lemière, et al.. A Global Constraint for Closed Frequent Pattern Mining
. CP: Principles and Practice of Constraint Programming, Sep 2016, Toulouse, France. pp.333-349, ⟨10.1007/978-3-319-44953-1_22⟩. ⟨lirmm-01374719⟩

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