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

研究成果: Article

2 引用 (Scopus)

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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.

元の言語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
発行部数2
DOI
出版物ステータスPublished - 2014 3

ASJC Scopus subject areas

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

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