Relational fuzzy c-lines clustering derived from kernelization of fuzzy c-lines

研究成果: Article査読

2 被引用数 (Scopus)

抄録

In this paper, two linear fuzzy clustering algorithms are proposed for relational data based on kernel fuzzy c-means, in which the prototypes of clusters are given by lines spanned in a feature space defined by the kernel which is derived from a given relational data. The proposed algorithms contrast the conventional method in which the prototypes of clusters are given by lines spanned by two representative objects. Through numerical examples, it is shown that the proposed algorithms can capture local sub-structures in relational data.

本文言語English
ページ(範囲)175-181
ページ数7
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
18
2
DOI
出版ステータスPublished - 2014 3

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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