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Generalizing Itemset Mining in a Constraint Programming Setting

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Inductive Databases and Constraint-Based Data Mining

Abstract

In recent years, a large number of algorithms have been proposed for finding set patterns in boolean data. This includes popular mining tasks based on, for instance, frequent (closed) itemsets. In this chapter, we develop a common framework in which these algorithms can be studied thanks to the principles of constraint programming. We show how such principles can be applied both in specialized and general solvers.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Informatics, Vilnius University, Naugarduko St. 24, 03225, Vilnius, Lithuania

    Jérémy Besson

  2. CNRS, INRIA INSA-Lyon, LIRIS Combining, Université de Lyon, UMR5205, 69621, Lyon, France

    Jean-François Boulicaut

  3. Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001, Leuven, Belgium

    Tias Guns & Siegfried Nijssen

Authors
  1. Jérémy Besson
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  2. Jean-François Boulicaut
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  3. Tias Guns
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  4. Siegfried Nijssen
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Corresponding author

Correspondence to Jérémy Besson .

Editor information

Editors and Affiliations

  1. , Department of Knowledge Technologies, Jozef Stefan Institute, Jamova 39, Ljubljana, 1000, Slovenia

    Sašo Džeroski

  2. , Mathematics and Computer Science, University of Antwerp, Middelheimlaan 1, Antwerpen, B-2020, Belgium

    Bart Goethals

  3. , Dept. of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, Ljubljana, SI-1000, Slovenia

    Panče Panov

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Besson, J., Boulicaut, JF., Guns, T., Nijssen, S. (2010). Generalizing Itemset Mining in a Constraint Programming Setting. In: Džeroski, S., Goethals, B., Panov, P. (eds) Inductive Databases and Constraint-Based Data Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7738-0_5

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  • DOI: https://doi.org/10.1007/978-1-4419-7738-0_5

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-7737-3

  • Online ISBN: 978-1-4419-7738-0

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