Semi-supervised fuzzy c-means algorithm by revising dissimilarity between data

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We propose two approaches for semi-supervised FCM with soft pairwise constraints. One applies NERFCM to the revised dissimilarity matrix by pairwise constraints. The other applies K-FCM with a dissimilarity-based kernel function, revising the dissimilarity matrix based on whether data in the same cluster may be close to each other or the data in the different clusters may be apart from each other. Propagating given pairwise constraints to unconstrained data is done when given constraints are not sufficient to obtain the desired clustering result. Numerical examples show that the proposed algorithms are valid.

Original languageEnglish
Pages (from-to)95-101
Number of pages7
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume15
Issue number1
Publication statusPublished - 2011 Jan

Keywords

  • Fuzzy c-means
  • Kernel
  • Relational clustering
  • Semi-supervised clustering

ASJC Scopus subject areas

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

Cite this

Semi-supervised fuzzy c-means algorithm by revising dissimilarity between data. / Kanzawa, Yuchi; Endo, Yasunori; Miyamoto, Sadaaki.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 15, No. 1, 01.2011, p. 95-101.

Research output: Contribution to journalArticle

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