Fuzzy clustering method for spherical data based on q-divergence

Masayuki Higashi, Tadafumi Kondo, Yuchi Kanzawa

Research output: Contribution to journalArticle

Abstract

This study presents a fuzzy clustering algorithm for classifying spherical data based on q-divergence. First, it is shown that a conventional method for vectorial data is equivalent to the regularization of another conventional method using q-divergence. Next, based on the knowledge that q-divergence is a generalization of Kullback-Leibler (KL)-divergence and that there is a conventional fuzzy clustering method for classifying spherical data based on KL-divergence, a fuzzy clustering algorithm for spherical data is derived based on q-divergence. This algorithm uses an optimization problem built by extending KL-divergence in the conventional method to q-divergence. Finally, some numerical experiments are conducted to verify the proposed methods.

Original languageEnglish
Pages (from-to)561-570
Number of pages10
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume23
Issue number3
DOIs
Publication statusPublished - 2019 May 1

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Fuzzy clustering
Clustering algorithms
Experiments

Keywords

  • Fuzzy clustering
  • KL-divergence
  • Q-divergence
  • Spherical data

ASJC Scopus subject areas

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

Cite this

Fuzzy clustering method for spherical data based on q-divergence. / Higashi, Masayuki; Kondo, Tadafumi; Kanzawa, Yuchi.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 23, No. 3, 01.05.2019, p. 561-570.

Research output: Contribution to journalArticle

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