Bezdek-type fuzzified co-clustering algorithm

Research output: Contribution to journalArticle

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

Fingerprint

Clustering algorithms
Fuzzy clustering
Entropy

Keywords

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

ASJC Scopus subject areas

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

Cite this

Bezdek-type fuzzified co-clustering algorithm. / Kanzawa, Yuchi.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 19, No. 6, 2015, p. 852-860.

Research output: Contribution to journalArticle

@article{e6eee831da714b22ac5fad9110e7a4b4,
title = "Bezdek-type fuzzified co-clustering algorithm",
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.",
keywords = "Bezdek-type fuzzification, Fuzzy co-clustering, Spectral clustering",
author = "Yuchi Kanzawa",
year = "2015",
language = "English",
volume = "19",
pages = "852--860",
journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics",
issn = "1343-0130",
publisher = "Fuji Technology Press",
number = "6",

}

TY - JOUR

T1 - Bezdek-type fuzzified co-clustering algorithm

AU - Kanzawa, Yuchi

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Bezdek-type fuzzification

KW - Fuzzy co-clustering

KW - Spectral clustering

UR - http://www.scopus.com/inward/record.url?scp=84954307725&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84954307725&partnerID=8YFLogxK

M3 - Article

VL - 19

SP - 852

EP - 860

JO - Journal of Advanced Computational Intelligence and Intelligent Informatics

JF - Journal of Advanced Computational Intelligence and Intelligent Informatics

SN - 1343-0130

IS - 6

ER -