Fuzzy Subspace Clustering

Conference paper

DOI: 10.1007/978-3-642-01044-6_8

Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)
Cite this paper as:
Borgelt C. (2009) Fuzzy Subspace Clustering. In: Fink A., Lausen B., Seidel W., Ultsch A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg


In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset. In this paper I study such an approach for fuzzy clustering, which is based on the idea to transfer an alternative to the fuzzifier (Klawonn and Höppner, What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier, In: Proc. 5th Int. Symp. on Intelligent Data Analysis, 254–264, Springer, Berlin, 2003) to attribute weighting fuzzy clustering (Keller and Klawonn, Int J Uncertain Fuzziness Knowl Based Syst 8:735–746, 2000). In addition, by reformulating Gustafson–Kessel fuzzy clustering, a scheme for weighting and selecting principal axes can be obtained. While in Borgelt (Feature weighting and feature selection in fuzzy clustering, In: Proc. 17th IEEE Int. Conf. on Fuzzy Systems, IEEE Press, Piscataway, NJ, 2008) I already presented such an approach for a global selection of attributes and principal axes, this paper extends it to a cluster-specific selection, thus arriving at a fuzzy subspace clustering algorithm (Parsons, Haque, and Liu, 2004).


Feature selection Feature weighting Fuzzifier alternative Fuzzy clustering Subspace clustering 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.European Center for Soft Computing, c/ Gonzalo Gutiérrez Quirós s/nMieresSpain

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