Fuzzy clustering method for spherical data based on q-divergence

Masayuki Higashi, Tadafumi Kondo, Yuchi Kanzawa

研究成果: Article

1 引用 (Scopus)

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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.

元の言語English
ページ(範囲)561-570
ページ数10
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
23
発行部数3
DOI
出版物ステータスPublished - 2019 5 1

ASJC Scopus subject areas

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

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