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Fuzzy Clustering Based on Modified Distance Measures

  • Frank Klawonn
  • Annette Keller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)

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.

Keywords

Distance Function Data Vector Fuzzy Cluster Membership Degree Fuzzy Partition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Frank Klawonn
    • 1
  • Annette Keller
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceOstfriesland University of Applied SciencesEmdenGermany
  2. 2.Institute for Flight GuidanceGerman Aerospace CenterBraunschweigGermany

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