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Local Pattern Detection pp 135–152Cite as

Local Pattern Discovery in Array-CGH Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3539))

Abstract

We report in this paper about our practice of frequent pattern discovery algorithms in the context of mining biological data related to genomic alterations in cancer. A number of frequent item set methods have already been successfully applied to various biological data obtained from large scale analyses (see for instance [4] for SAGE data, [20,22,26] for gene expression data), and all of these have to face the peculiarity of such data wrt standard basket analysis data, namely that the number of observations is low wrt the number of attributes.

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Author information

Authors and Affiliations

  1. LRI , UMR 8623, Université Paris Sud, bât 490, 91405, Orsay cedex, France

    Céline Rouveirol

  2. Institut Curie, UMR 144, 26 rue d’Ulm, 75248, Paris cedex 05, France

    Céline Rouveirol & Francois Radvanyi

Authors
  1. Céline Rouveirol
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  2. Francois Radvanyi
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Editor information

Editors and Affiliations

  1. Computer Science VIII, artificial Intelligence Unit, Technische Universität Dortmund, 44221, Dortmund, Germany

    Katharina Morik

  2. INSA-Lyon, LIRIS CNRS UMR5205, F-69621, Villeurbanne, France

    Jean-François Boulicaut

  3. Department of Computer Science, Universiteit Utrecht,  

    Arno Siebes

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© 2005 Springer-Verlag Berlin Heidelberg

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Rouveirol, C., Radvanyi, F. (2005). Local Pattern Discovery in Array-CGH Data. In: Morik, K., Boulicaut, JF., Siebes, A. (eds) Local Pattern Detection. Lecture Notes in Computer Science(), vol 3539. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504245_9

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  • DOI: https://doi.org/10.1007/11504245_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26543-6

  • Online ISBN: 978-3-540-31894-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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