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Identifying Multiple Cluster Structures Through Latent Class Models

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From Data and Information Analysis to Knowledge Engineering

Abstract

Many studies addressing the problem of selecting or weighting variables for cluster analysis assume that all the variables define a unique classification of units. However it is also possible that different classifications of units can be obtained from different subsets of variables. In this paper this problem is considered from a model-based perspective. Limitations and drawbacks of standard latent class cluster analysis are highlighted and a new procedure able to overcome these difficulties is proposed. The results obtained from the application of this procedure on simulated and real data sets are presented and discussed.

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Author information

Authors and Affiliations

  1. Dipartimento di Scienze Statistiche, Università di Bologna, 40126, Bologna, Italy

    Giuliano Galimberti & Gabriele Soffritti

Authors
  1. Giuliano Galimberti
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  2. Gabriele Soffritti
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Editor information

Editors and Affiliations

  1. Institut für Technische und Betriebliche Informationssysteme, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Myra Spiliopoulou

  2. Institut für Wissens- und Sprachverarbeitung, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Rudolf Kruse , Christian Borgelt  & Andreas Nürnberger ,  & 

  3. Institut für Entscheidungstheorie und Unternehmensforschung, Universität Karlsruhe (TH), 76128, Karlsruhe

    Wolfgang Gaul

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© 2006 Springer Berlin · Heidelberg

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Galimberti, G., Soffritti, G. (2006). Identifying Multiple Cluster Structures Through Latent Class Models. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_20

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