Relational fuzzy c-means and kernel fuzzy c-means using a quadratic programming-based object-wise β-spread transformation

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

Abstract

Clustering methods of relational data are often based on the assumption that a given set of relational data is Euclidean, and kernelized clustering methods are often based on the assumption that a given kernel is positive semidefinite. In practice, non-Euclidean relational data and an indefinite kernel may arise, and a β-spread transformation was proposed for such cases, which modified a given set of relational data or a given a kernel Gram matrix such that the modified β value is common to all objects. In this paper, we propose a quadratic programming-based object-wise β-spread transformation for use in both relational and kernelized fuzzy c-means clustering. The proposed system retains the given data better than conventional methods, and numerical examples show that our method is efficient for both relational and kernel fuzzy c-means.

Original languageEnglish
Title of host publicationKnowledge and Systems Engineering - Proceedings of the 5th International Conference, KSE 2013
EditorsThierry Denoeux, Van-Nam Huynh, Dang Hung Tran, Anh Cuong Le, Son Bao Pham
PublisherSpringer Verlag
Pages29-43
Number of pages15
ISBN (Electronic)9783319028200
DOIs
Publication statusPublished - 2014
Event5th International Conference on Knowledge and Systems Engineering, KSE 2013 - Hanoi, Viet Nam
Duration: 2013 Oct 172013 Oct 19

Publication series

NameAdvances in Intelligent Systems and Computing
Volume245
ISSN (Print)2194-5357

Other

Other5th International Conference on Knowledge and Systems Engineering, KSE 2013
CountryViet Nam
CityHanoi
Period13/10/1713/10/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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