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

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)1057-1064
Number of pages8
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume15
Issue number8
Publication statusPublished - 2011 Oct

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Entropy
Supervisory personnel
Clustering algorithms
Numerical methods
Experiments

Keywords

  • Fuzzy c-means
  • Semi-supervised clustering

ASJC Scopus subject areas

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

Cite this

KL-divergence-based and manhattan distance-based semisupervised entropy-regularized fuzzy c-means. / Kanzawa, Yuchi; Endo, Yasunori; Miyamoto, Sadaaki.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 15, No. 8, 10.2011, p. 1057-1064.

Research output: Contribution to journalArticle

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