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Dynamic Symmetry Breaking in Itemset Mining

  • Conference paper
Artificial Intelligence and Symbolic Computation (AISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8884))

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Abstract

The concept of symmetry has been extensively studied in the field of constraint programming and in the propositional satisfiability. We are interested here, by the detection and elimination of local and global symmetries in the item-set mining problem. Recent works have provided effective encodings as Boolean constraints for these data mining tasks and some idea on symmetry elimination in this area begin to appear, but still few and the techniques presented are often on global symmetry that is detected and eliminated statically in a preprocessing phase. In this work we study the notion of local symmetry and compare it to global symmetry for the itemset mining problem. We show how local symmetries of the boolean encoding can be detected dynamically and give some properties that allow to eliminate theses symmetries in SAT-based itemset mining solvers in order to enhance their efficiency.

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Author information

Authors and Affiliations

  1. Aix-Marseille Université, Laboratoire des Sciences de l’information et des Systèmes (LSIS), Domaine universitaire de Saint Jérôme, Avenue Escadrille Normandie Niemen, 13397, MARSEILLE Cedex 20, France

    Belaïd Benhamou

  2. Centre de Recherche en Informatique de Lens (CRIL), Université d’Artois, Rue Jean Souvenir, SP 18, F62307, Lens Cedex, France

    Belaïd Benhamou

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  1. Belaïd Benhamou
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Editors and Affiliations

  1. Department of Information Technologies, University of Huelva, Crta. Palos de la Frontera, s/n, 21810, Palos de la Frontera, Spain

    Gonzalo A. Aranda-Corral

  2. Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131, Karlsruhe, Germany

    Jacques Calmet

  3. Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Sevilla, Avda. Reina Mercedes, s/n, 41012, Sevilla, Spain

    Francisco J. Martín-Mateos

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Benhamou, B. (2014). Dynamic Symmetry Breaking in Itemset Mining. In: Aranda-Corral, G.A., Calmet, J., Martín-Mateos, F.J. (eds) Artificial Intelligence and Symbolic Computation. AISC 2014. Lecture Notes in Computer Science(), vol 8884. Springer, Cham. https://doi.org/10.1007/978-3-319-13770-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-13770-4_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13769-8

  • Online ISBN: 978-3-319-13770-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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