Relational fuzzy c-lines derived from kernel fuzzy c-lines

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, three 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 denned by the kernel which 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
Title of host publication6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
Pages1561-1566
Number of pages6
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012 - Kobe, Japan
Duration: 2012 Nov 202012 Nov 24

Publication series

Name6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012

Conference

Conference2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012
CountryJapan
CityKobe
Period12/11/2012/11/24

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Kanzawa, Y. (2012). Relational fuzzy c-lines derived from kernel fuzzy c-lines. In 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012 (pp. 1561-1566). [6505052] (6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012). https://doi.org/10.1109/SCIS-ISIS.2012.6505052