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Effcient Local Search in Conceptual Clustering

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Discovery Science (DS 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2226))

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Abstract

In this paper, we consider unsupervised clustering as a combinatorial optimization problem. We focus on the use of Local Search procedures to optimize an association coefficient whose aim is to construct a couple of conceptual partitions, one on the set of objects and the other one on the set of attribute-value pairs. We present a study of the variation of the function in order to decrease the complexity of local search and to propose stochastic local search. Performances of the given algorithms are tested on synthetic data sets and the real data set Vote taken from the UCI Irvine repository.

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Author information

Authors and Affiliations

  1. Laboratoire d’Analyse des Systémes de Santé, Université Lyon 1, UMR 5823, bât 101, 43 bd du 11 nov. 1918, 69622, Villeurbanne cedex, FRANCE

    Céline Robardet & Fabien Feschet

Authors
  1. Céline Robardet
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  2. Fabien Feschet
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Editor information

Editors and Affiliations

  1. DFKI GmbH Saarbrücken, 66123, Saarbrücken, Germany

    Klaus P. Jantke

  2. Department of Informatics, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, 812-8581, Fukuoka, Japan

    Ayumi Shinohara

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© 2001 Springer-Verlag Berlin Heidelberg

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Robardet, C., Feschet, F. (2001). Effcient Local Search in Conceptual Clustering. In: Jantke, K.P., Shinohara, A. (eds) Discovery Science. DS 2001. Lecture Notes in Computer Science(), vol 2226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45650-3_28

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  • DOI: https://doi.org/10.1007/3-540-45650-3_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42956-2

  • Online ISBN: 978-3-540-45650-6

  • eBook Packages: Springer Book Archive

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