Title :
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
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;
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
DOI :
10.1109/AFSS.1996.583630
