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Understanding and Controlling the Membership Degrees in Fuzzy Clustering

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From Data and Information Analysis to Knowledge Engineering

Abstract

Fuzzy cluster analysis uses membership degrees to assign data objects to clusters in order to better handle ambiguous data that share properties of different clusters. However, the introduction of membership degrees requires a new parameter called fuzzifier. In this paper the good and bad effects of the fuzzifier on the clustering results are analysed and based on these considerations a more general approach to fuzzy clustering is proposed, providing better control on the membership degrees and their influence in fuzzy cluster analysis.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Applied Sciences Braunschweig/Wolfenbuettel, D-38302, Wolfenbuettel, Germany

    Frank Klawonn

Authors
  1. Frank Klawonn
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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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Klawonn, F. (2006). Understanding and Controlling the Membership Degrees in Fuzzy Clustering. 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_54

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