Bezdek-type fuzzified co-clustering algorithm

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10 Citations (Scopus)

Abstract

In this study, two co-clustering algorithms based on Bezdek-type fuzzification of fuzzy clustering are proposed for categorical multivariate data. The two proposed algorithms are motivated by the fact that there are only two fuzzy co-clustering methods currently available - entropy regularization and quadratic regularization - whereas there are three fuzzy clustering methods for vectorial data: entropy regularization, quadratic regularization, and Bezdek-type fuzzification. The first proposed algorithm forms the basis of the second algorithm. The first algorithm is a variant of a spherical clustering method, with the kernelization of a maximizing model of Bezdek-type fuzzy clustering with multi-medoids. By interpreting the first algorithm in this way, the second algorithm, a spectral clustering approach, is obtained. Numerical examples demonstrate that the proposed algorithms can produce satisfactory results when suitable parameter values are selected.

Original languageEnglish
Pages (from-to)852-860
Number of pages9
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume19
Issue number6
Publication statusPublished - 2015

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Keywords

  • Bezdek-type fuzzification
  • Fuzzy co-clustering
  • Spectral clustering

ASJC Scopus subject areas

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

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