On FNM-based and RFCM-based fuzzy co-clustering algorithms

Yuchi Kanzawa, Yasunori Endo

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

1 Citation (Scopus)

Abstract

In this paper, some types of fuzzy co-clustering algorithms are proposed. First, it is shown that the common base of the objective function for quadratic-regularized fuzzy co-clustering and entropy-regularized fuzzy co-clustering is very similar to the base for quadratic-regularized fuzzy nonmetric model and entropy-regularized fuzzy nonmetric model, respectively. Next, it is shown that the above mentioned non-sense clustering problem in previously proposed fuzzy co-clustering algorithms is identical to that in fuzzy nonmetric model algorithms, in the case that all dissimilarities among rows and columns are zero. Based on the above discussion, a method is proposed applying fuzzy nonmetric model after all dissimilarities among rows and columns are non-zero. Furthermore, since relational fuzzy c-means is similar to fuzzy nonmetric model, in the sense that both methods are designed for homogenous relational data, a method is proposed applying relational fuzzy c-means after setting all dissimilarities among rows and columns to some non-zero value. An illustrative numerical example is presented for the proposed methods.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
DOIs
Publication statusPublished - 2012 Oct 23
Event2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012 - Brisbane, QLD, Australia
Duration: 2012 Jun 102012 Jun 15

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
CountryAustralia
CityBrisbane, QLD
Period12/6/1012/6/15

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ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

Cite this

Kanzawa, Y., & Endo, Y. (2012). On FNM-based and RFCM-based fuzzy co-clustering algorithms. In 2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012 [6250781] (IEEE International Conference on Fuzzy Systems). https://doi.org/10.1109/FUZZ-IEEE.2012.6250781