A distributed approach to fuzzy clustering by genetic algorithms

Author

Wei, Chih-Hsiu ; Fahn, Chin-shyurng

Author_Institution

Dept. of Electr. Eng. & Technol., Nat. Taiwan Inst. of Technol., Taipei, Taiwan

fYear

1996

fDate

11-14 Dec 1996

Firstpage

350

Lastpage

357

Abstract

Fuzzy clustering (c-means) is a widely known unsupervised clustering algorithm, but it can not guarantee to find the global minimum, because it approximates the minimum of an objective function by the iterative method in solving the differentiation problem, starting from a given point. For overcoming this drawback, we incorporate the genetic search strategies in the fuzzy clustering algorithm to explore the data space from a multiple-point concept. The direct application of the genetic algorithms to the fuzzy clustering is not suitable, because sometimes the data set is enormous. Under this situation, the chromosome would be too long, so a distributed approach to fuzzy clustering by genetic algorithms is proposed to divide the huge search space into many small ones. The simulation results show our algorithm works fine

Keywords

fuzzy logic; genetic algorithms; query formulation; c-means; chromosome; differentiation problem; distributed approach; fuzzy clustering; fuzzy clustering algorithm; genetic algorithms; genetic search strategies; simulation results; unsupervised clustering algorithm; Biological cells; Clustering algorithms; Clustering methods; Fuzzy sets; Genetic algorithms; Iterative algorithms; Iterative methods; Partitioning algorithms; Space exploration; Space technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems Symposium, 1996. Soft Computing in Intelligent Systems and Information Processing., Proceedings of the 1996 Asian

Conference_Location

Kenting

Print_ISBN

0-7803-3687-9

Type

conf

DOI

10.1109/AFSS.1996.583630

Filename

583630