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Fuzzy Clustering Based on Modified Distance Measures

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Advances in Intelligent Data Analysis (IDA 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1642))

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Abstract

The well-known fuzzy c-means algorithm is an objective function based fuzzy clustering technique that extends the classical k-means method to fuzzy partitions. By replacing the Euclidean distance in the objective function other cluster shapes than the simple (hyper-)spheres of the fuzzy c-means algorithm can be detected, for instance ellipsoids, lines or shells of circles and ellipses. We propose a modified distance function that is based on the dot product and allows to detect a new kind of cluster shape and also lines and (hyper-)planes.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Ostfriesland University of Applied Sciences, Constantiaplatz 4, D-26723, Emden, Germany

    Frank Klawonn

  2. Institute for Flight Guidance, German Aerospace Center, Lilienthalplatz 7, D-38108, Braunschweig, Germany

    Annette Keller

Authors
  1. Frank Klawonn
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  2. Annette Keller
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Imperial College, Huxley Building 180 Queen’s Gate, London, SW7 2BZ, UK

    David J. Hand

  2. Leiden Institute for Advanced Computer Science, Leiden University, 2300, RA Leiden, The Netherlands

    Joost N. Kok

  3. Berkeley Initiative in Soft Computing, University of California at Berkeley, 329 Soda Hall, Berkeley, CA, 94720, USA

    Michael R. Berthold

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© 1999 Springer-Verlag Berlin Heidelberg

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Klawonn, F., Keller, A. (1999). Fuzzy Clustering Based on Modified Distance Measures. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_25

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  • DOI: https://doi.org/10.1007/3-540-48412-4_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66332-4

  • Online ISBN: 978-3-540-48412-7

  • eBook Packages: Springer Book Archive

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