KL-divergence-based and manhattan distance-based semisupervised entropy-regularized fuzzy c-means

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

研究成果: Article査読

抄録

In this paper, two types of semi-supervised fuzzy cmeans algorithms are proposed. One feature of proposed algorithms is that they are based on an entropyregularized fuzzy c-means clustering algorithm, while conventional algorithms are based on standard fuzzy c-means. Another feature of proposed algorithms is that the membership updating equation can be obtained explicitly with any fuzzifier parameter value, while in conventional methods, the updating equation must be solved by some numerical method or by a numerically complex refinement with almost all fuzzifier parameters. The influence of supervisor-parameter and fuzzifier parameter on clustering results are discussed based on numerical experiments and compared to the conventional method, demonstrating the feasibility of proposed algorithms.

本文言語English
ページ(範囲)1057-1064
ページ数8
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
15
8
DOI
出版ステータスPublished - 2011 10

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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