Fuzzy co-clustering algorithms based on fuzzy relational clustering and TIBA imputation

Research output: Contribution to journalArticle

9 Citations (Scopus)


In this paper, two types of fuzzy co-clustering algorithms are proposed. First, it is shown that the base of the objective function for the conventional fuzzy co-clustering method is very similar to the base for entropy-regularized fuzzy nonmetric model. Next, it is shown that the non-sense clustering problem in the conventional 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 this discussion, a method is proposed applying entropy-regularized fuzzy nonmetric model after all dissimilarities among rows and columns are set to some values using a TIBA imputation technique. Furthermore, since relational fuzzy cmeans is similar to fuzzy nonmetricmodel, in the sense that both methods are designed for homogeneous relational data, a method is proposed applying entropyregularized relational fuzzy c-means after imputing all dissimilarities among rows and columns with TIBA. Some numerical examples are presented for the proposed methods.

Original languageEnglish
Pages (from-to)182-189
Number of pages8
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Issue number2
Publication statusPublished - 2014 Mar 1



  • Entropyregularized relational fuzzy c-means
  • Fuzzy clustering for entropy-regularized fuzzy nonmetric model
  • Fuzzy co-clustering
  • TIBA

ASJC Scopus subject areas

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

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