On kernelization for a maximizing model of bezdek-like spherical fuzzy c-means clustering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

In this study, we propose three modifications for a maximizing model of spherical Bezdek-type fuzzy c-means clustering (msbFCM). First, we kernelize msbFCM (K-msbFCM). The original msbFCM can only be applied to objects on the first quadrant of the unit hypersphere, whereas its kernelized form can be applied to a wider class of objects. The second modification is a spectral clustering approach to K-msbFCM using a certain assumption. This approach solves the local convergence problem in the original algorithm. The third modification is to construct a model providing the exact solution of the spectral clustering approach. Numerical examples demonstrate that the proposed methods can produce good results for clusters with nonlinear borders when an adequate parameter value is selected.

Original languageEnglish
Title of host publicationModeling Decisions forArtificial Intelligence - 11th International Conference, MDAI 2014, Proceedings
EditorsVicenç Torra, Yasuo Narukawa, Yasunori Endo
PublisherSpringer Verlag
Pages108-121
Number of pages14
ISBN (Electronic)9783319120539
DOIs
Publication statusPublished - 2014
Event11th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2014 - Tokyo , Japan
Duration: 2014 Oct 292014 Oct 31

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8825
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2014
CountryJapan
CityTokyo
Period14/10/2914/10/31

Keywords

  • Fuzzy c-means clustering
  • Kernelization
  • Spectral clustering approach

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kanzawa, Y. (2014). On kernelization for a maximizing model of bezdek-like spherical fuzzy c-means clustering. In V. Torra, Y. Narukawa, & Y. Endo (Eds.), Modeling Decisions forArtificial Intelligence - 11th International Conference, MDAI 2014, Proceedings (pp. 108-121). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8825). Springer Verlag. https://doi.org/10.1007/978-3-319-12054-6_10