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A General Approach to Test the Pertinence of a Consensus Classification

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Data Analysis, Classification, and Related Methods

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Abstract

Many techniques have been proposed to combine classifications defined on the same set of objects. All the methods that have been developed are designed to return a solution, but validation of the solution is seldom performed. In this paper we propose a general approach to test the pertinence of a consensus classification and discuss the choices that one has to make at each step of the method.

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Author information

Authors and Affiliations

  1. École des sciences de la gestion, Université du Québec à Montréal, C.P. 8888, Succ. Centre-Ville, Montréal, Québec, H3C 3P8, Canada

    Guy Cucumel

  2. Département de sciences biologiques, Université de Montréal, C.P. 6128, Succ. Centre-Ville, Montréal, Québec, H3C 3J7, Canada

    François-Joseph Lapointe

Authors
  1. Guy Cucumel
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  2. François-Joseph Lapointe
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Editor information

Editors and Affiliations

  1. University of Groningen Heymans Institute (PA), Grote Kruisstraat 2/1, NL-9712 TS, Groningen, The Netherlands

    Henk A. L. Kiers

  2. Facultés Universitaires Notre-Dame de la Paix, University of Namur, Rempart de la Vierge, 8, B-5000, Namur, Belgium

    Jean-Paul Rasson (Directeur du Department de Mathématique) (Directeur du Department de Mathématique)

  3. Data Theory Group Department of Education, Leiden University, P.O. Box 9555, NL-2300 RB, Leiden, The Netherlands

    Patrick J. F. Groenen

  4. Lehrstuhl für Wirtschaftsinformatik III Schloß, University of Mannheim, D-68131, Mannheim, Germany

    Martin Schader

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© 2000 Springer-Verlag Berlin · Heidelberg

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Cucumel, G., Lapointe, FJ. (2000). A General Approach to Test the Pertinence of a Consensus Classification. In: Kiers, H.A.L., Rasson, JP., Groenen, P.J.F., Schader, M. (eds) Data Analysis, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59789-3_20

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  • DOI: https://doi.org/10.1007/978-3-642-59789-3_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67521-1

  • Online ISBN: 978-3-642-59789-3

  • eBook Packages: Springer Book Archive

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