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Database Transposition for Constrained (Closed) Pattern Mining

  • Conference paper
Knowledge Discovery in Inductive Databases (KDID 2004)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3377))

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Abstract

Recently, different works proposed a new way to mine patterns in databases with pathological size. For example, experiments in genome biology usually provide databases with thousands of attributes (genes) but only tens of objects (experiments). In this case, mining the “transposed” database runs through a smaller search space, and the Galois connection allows to infer the closed patterns of the original database. We focus here on constrained pattern mining for those unusual databases and give a theoretical framework for database and constraint transposition. We discuss the properties of constraint transposition and look into classical constraints. We then address the problem of generating the closed patterns of the original database satisfying the constraint, starting from those mined in the “transposed” database. Finally, we show how to generate all the patterns satisfying the constraint from the closed ones.

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Author information

Authors and Affiliations

  1. Équipe Universitaire de Recherche en Informatique de St-Etienne, Université de St-Etienne, France

    Baptiste Jeudy

  2. GREYC – CNRS UMR 6072, Université de Caen Basse-Normandie, France

    François Rioult

Authors
  1. Baptiste Jeudy
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  2. François Rioult
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Editor information

Editors and Affiliations

  1. Mathematics and computer Science Department, University of Antwerp, Middelheimlaan 1, 2020, Antwerp, Belgium

    Bart Goethals

  2. Department of Computer Science, Universiteit Utrecht,  

    Arno Siebes

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© 2005 Springer-Verlag Berlin Heidelberg

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Jeudy, B., Rioult, F. (2005). Database Transposition for Constrained (Closed) Pattern Mining. In: Goethals, B., Siebes, A. (eds) Knowledge Discovery in Inductive Databases. KDID 2004. Lecture Notes in Computer Science, vol 3377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31841-5_6

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  • DOI: https://doi.org/10.1007/978-3-540-31841-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25082-1

  • Online ISBN: 978-3-540-31841-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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