Fuzzy clustering method for spherical data based on q-divergence

Masayuki Higashi, Tadafumi Kondo, Yuchi Kanzawa

研究成果: Article査読

3 被引用数 (Scopus)

抄録

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.

本文言語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
3
DOI
出版ステータスPublished - 2019 5

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能

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