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Fuzzy Structural Classification Methods

  • Mika Sato-Ilic
  • Tomoyuki Kuwata
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)

Abstract

This paper presents several fuzzy clustering methods based on self-organized similarity (or dissimilarity). Self-organized similarity (or dissimilarity) has been proposed in order to consider not only the similarity (or dissimilarity) between a pair of objects but also the similarity (or dissimilarity) between the classification structures of objects. Depending on how the similarity (or dissimilarity) of the classification structures cope with the fuzzy clustering methods, the results will be different from each other. This paper discusses this difference and shows several numerical examples.

Keywords

Fuzzy Cluster Clear Result Landsat Data Fuzzy Cluster Method Dissimilarity Data 
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 2006

Authors and Affiliations

  • Mika Sato-Ilic
    • 1
  • Tomoyuki Kuwata
    • 1
  1. 1.Faculty of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan

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