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

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2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)175-181
Number of pages7
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume18
Issue number2
Publication statusPublished - 2014 Mar

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Fuzzy clustering
Clustering algorithms

Keywords

  • Kernel fuzzy clustering
  • Relational fuzzy clustering

ASJC Scopus subject areas

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

Cite this

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abstract = "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.",
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