Power-regularized fuzzy clustering for spherical data

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, a power-regularization-based fuzzy clustering method is proposed for spherical data. Power regularization has not been previously applied to fuzzy clustering for spherical data. The proposed method is transformed to the conventional fuzzy clustering method, entropy-regularized fuzzy clustering for spherical data (eFCS), for a specified fuzzification parameter value. Numerical experiments on two artificial datasets reveal the properties of the proposed method. Furthermore, numerical experiments on four real datasets indicate that this method is more accurate than the conventional fuzzy clustering methods: standard fuzzy clustering for spherical data (sFCS) and eFCS.

Original languageEnglish
Pages (from-to)163-171
Number of pages9
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume22
Issue number2
DOIs
Publication statusPublished - 2018 Mar 1

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Fuzzy clustering
Entropy
Experiments

Keywords

  • Fuzzy clustering
  • Powerregularization
  • Spherical data

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

Power-regularized fuzzy clustering for spherical data. / Kanzawa, Yuchi.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 22, No. 2, 01.03.2018, p. 163-171.

Research output: Contribution to journalArticle

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