On Bezdek-type fuzzy clustering for categorical multivariate data

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

4 Citations (Scopus)

Abstract

In this study, five co-clustering algorithms based on Bezdek-type fuzzification of fuzzy clustering are propsoed for categorical multivariate data. The algorithms are motivated the fact that, there are only two fuzzy co-clustering methods - 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 algorithm proposed forms the basis of other two algorithms. By interpreting the first algorithm as a variant of a maximizing model of fuzzy multi-medoids, the second algorithm, a spectral clustering approach is obtained. Further, by slightly revising the objective function of the first algorithm, the third algorithm, another spectral clustering approach, is also obtained. The fourth algorithm is obtained by Bezdek-type fuzzification for row-membership whereas entropy-regularization for column-mebership. The fifth algorithm is a spectral clustering approach to the fourth algorithm. Numerical examples demonstrate that the proposed algorithms can produce satisfactory results when suitable parameter values are selected.

Original languageEnglish
Title of host publication2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages694-699
Number of pages6
ISBN (Print)9781479959556
DOIs
Publication statusPublished - 2014 Feb 18
Event2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 - Kitakyushu, Japan
Duration: 2014 Dec 32014 Dec 6

Other

Other2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
CountryJapan
CityKitakyushu
Period14/12/314/12/6

Fingerprint

Fuzzy clustering
Entropy
Clustering algorithms

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Kanzawa, Y. (2014). On Bezdek-type fuzzy clustering for categorical multivariate data. In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 (pp. 694-699). [7044511] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SCIS-ISIS.2014.7044511

On Bezdek-type fuzzy clustering for categorical multivariate data. / Kanzawa, Yuchi.

2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 694-699 7044511.

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

Kanzawa, Y 2014, On Bezdek-type fuzzy clustering for categorical multivariate data. in 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014., 7044511, Institute of Electrical and Electronics Engineers Inc., pp. 694-699, 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014, Kitakyushu, Japan, 14/12/3. https://doi.org/10.1109/SCIS-ISIS.2014.7044511
Kanzawa Y. On Bezdek-type fuzzy clustering for categorical multivariate data. In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 694-699. 7044511 https://doi.org/10.1109/SCIS-ISIS.2014.7044511
Kanzawa, Yuchi. / On Bezdek-type fuzzy clustering for categorical multivariate data. 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 694-699
@inproceedings{5946233d395942fa85872ccf97e6f33f,
title = "On Bezdek-type fuzzy clustering for categorical multivariate data",
abstract = "In this study, five co-clustering algorithms based on Bezdek-type fuzzification of fuzzy clustering are propsoed for categorical multivariate data. The algorithms are motivated the fact that, there are only two fuzzy co-clustering methods - 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 algorithm proposed forms the basis of other two algorithms. By interpreting the first algorithm as a variant of a maximizing model of fuzzy multi-medoids, the second algorithm, a spectral clustering approach is obtained. Further, by slightly revising the objective function of the first algorithm, the third algorithm, another spectral clustering approach, is also obtained. The fourth algorithm is obtained by Bezdek-type fuzzification for row-membership whereas entropy-regularization for column-mebership. The fifth algorithm is a spectral clustering approach to the fourth algorithm. Numerical examples demonstrate that the proposed algorithms can produce satisfactory results when suitable parameter values are selected.",
author = "Yuchi Kanzawa",
year = "2014",
month = "2",
day = "18",
doi = "10.1109/SCIS-ISIS.2014.7044511",
language = "English",
isbn = "9781479959556",
pages = "694--699",
booktitle = "2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - On Bezdek-type fuzzy clustering for categorical multivariate data

AU - Kanzawa, Yuchi

PY - 2014/2/18

Y1 - 2014/2/18

N2 - In this study, five co-clustering algorithms based on Bezdek-type fuzzification of fuzzy clustering are propsoed for categorical multivariate data. The algorithms are motivated the fact that, there are only two fuzzy co-clustering methods - 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 algorithm proposed forms the basis of other two algorithms. By interpreting the first algorithm as a variant of a maximizing model of fuzzy multi-medoids, the second algorithm, a spectral clustering approach is obtained. Further, by slightly revising the objective function of the first algorithm, the third algorithm, another spectral clustering approach, is also obtained. The fourth algorithm is obtained by Bezdek-type fuzzification for row-membership whereas entropy-regularization for column-mebership. The fifth algorithm is a spectral clustering approach to the fourth algorithm. Numerical examples demonstrate that the proposed algorithms can produce satisfactory results when suitable parameter values are selected.

AB - In this study, five co-clustering algorithms based on Bezdek-type fuzzification of fuzzy clustering are propsoed for categorical multivariate data. The algorithms are motivated the fact that, there are only two fuzzy co-clustering methods - 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 algorithm proposed forms the basis of other two algorithms. By interpreting the first algorithm as a variant of a maximizing model of fuzzy multi-medoids, the second algorithm, a spectral clustering approach is obtained. Further, by slightly revising the objective function of the first algorithm, the third algorithm, another spectral clustering approach, is also obtained. The fourth algorithm is obtained by Bezdek-type fuzzification for row-membership whereas entropy-regularization for column-mebership. The fifth algorithm is a spectral clustering approach to the fourth algorithm. Numerical examples demonstrate that the proposed algorithms can produce satisfactory results when suitable parameter values are selected.

UR - http://www.scopus.com/inward/record.url?scp=84926631456&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84926631456&partnerID=8YFLogxK

U2 - 10.1109/SCIS-ISIS.2014.7044511

DO - 10.1109/SCIS-ISIS.2014.7044511

M3 - Conference contribution

AN - SCOPUS:84926631456

SN - 9781479959556

SP - 694

EP - 699

BT - 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014

PB - Institute of Electrical and Electronics Engineers Inc.

ER -