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 publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Pages29-43
Number of pages15
Volume245
ISBN (Print)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)21945357

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)

Cite this

Kanzawa, Y. (2014). Relational fuzzy c-means and kernel fuzzy c-means using a quadratic programming-based object-wise β-spread transformation. In Advances in Intelligent Systems and Computing (Vol. 245, pp. 29-43). (Advances in Intelligent Systems and Computing; Vol. 245). Springer Verlag. https://doi.org/10.1007/978-3-319-02821-7_5