A hybrid clustering algorithm incorporating fuzzy C-means into canonical genetic algorithm

Chen Jinshan; Wei Gang

Volume 24 Issue 2

Feb. 2002

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Chen Jinshan, Wei Gang. A hybrid clustering algorithm incorporating fuzzy C-means into canonical genetic algorithm[J]. Journal of Electronics & Information Technology, 2002, 24(2): 210-215.

Citation:

Chen Jinshan, Wei Gang. A hybrid clustering algorithm incorporating fuzzy C-means into canonical genetic algorithm[J]. Journal of Electronics & Information Technology, 2002, 24(2): 210-215.

Chen Jinshan, Wei Gang. A hybrid clustering algorithm incorporating fuzzy C-means into canonical genetic algorithm[J]. Journal of Electronics & Information Technology, 2002, 24(2): 210-215.

Citation:

Chen Jinshan, Wei Gang. A hybrid clustering algorithm incorporating fuzzy C-means into canonical genetic algorithm[J]. Journal of Electronics & Information Technology, 2002, 24(2): 210-215.

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A hybrid clustering algorithm incorporating fuzzy C-means into canonical genetic algorithm

Received Date: 2000-04-06
Rev Recd Date: 2000-12-27
Publish Date: 2002-02-19

Abstract

Abstract

A new Hybrid Clustering Algorithm (HCA) that incorporates the fuzzy C-means into the canonical genetic algorithm is proposed in this paper. The HCA speeds up convergence before the genetic algorithm reach the global optima, and eliminates fuzzy C-means trapped local minima by performing global search and local search alternatively. The experiments for clustering three data sets with different distributions show that the HCA has better generalization and effectiveness.

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References(1)

References

高新波,谢维信,模糊聚类理论发展及应用的研究进展.科学通报,1999,44(21),2241 2251[2]Wu Youshou, Ding Xiaoqing, A new clustering method for Chinese character recognition system using artificial neural networks, Chinese J. of Electronics, 1993, 2(3), 1-8.[3]J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact wellseparated clusters, Journal of Cybernetics, 1973, 3(1), 32-57.[4]J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, New York, Plenum Press, 1981, 43-93.[5]J.C. Bezdek, R. Hathaway, M. Sabin, W. Tucker, Convergence theory for fuzzy C-means, Counterexample and repairs, IEEE Trans. on SMC, 1987, 17(5), 873-877.[6]S.L. Chiu, Fuzzy model identification based on cluster estimation, J. Intelligent and Fuzzy Syst.,1994, 2(3), 267-278.[7]D. Chaudhuri, B. B. Chaudhuri, A novel multiseed nonhierarchical data clustering technique,IEEE Trans. on SMC, 1997, 27(5), 871-877.[8]S.Z. Selim, K. Alsultan, A simulated annealing algorithm for the clustering problem, Pattern Recognition, 1991, 24(10), 1003-1008.[9]K.S. Asultan, S. Seltan, A global algorithm for the fuzzy clustering problem, Pattern Recognition,1993, 26(9), 1357-1361.[10]贺前华,韦岗,陆以勤,基因算法研究进展,电子学报,1998,26(10),118-122[11]潘正君,康立山,陈毓屏,演化计算,北京,清华大学出版社,1998,1-43[12]李强,周济,连续解空间的复合遗传算法,科学通报,1998,43(24),2662-2668.[13]张青富,彭伟,吴少岩等,遗传算法+正交设计:一种新的全局优化算法,第4届中国人工智能联合学术会议论文集,北京,清华大学出版社,1996,127-133.[14]G. Rudolph, Convergence analysis of canonical genetic algorithms, IEEE Trans. on NN, 1994,5(1), 96-101.

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