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

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

研究成果: Article査読

4 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)95-101
ページ数7
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
15
1
DOI
出版ステータスPublished - 2011 1月

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

引用スタイル