Fuzzy C-means algorithm based on pso and mahalanobis distance

ASIA unversity > 資訊學院 > 生物資訊與醫學工程學系 > 期刊論文 > Item 310904400/8646

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Title:	Fuzzy C-means algorithm based on pso and mahalanobis distance
Authors:	Liu, Hsiang-Chuan;Yih, Jeng-Ming;Lin, Wen-Chih;Liu, Tung-Sheng
Contributors:	Department of Bioinformatics
Keywords:	Copying;Covariance matrix;Fuzzy clustering;Fuzzy rules;Fuzzy systems;Particle swarm optimization (PSO);Euclidean distance;FCM algorithm;Fuzzy C-means algorithms;G-K algorithm;Gustafson-Kessel;M-algorithms;Mahalanobis;Mahalanobis distances;Non-Spherical;Prior information;Real data sets;Singularity problems
Date:	2009-12
Issue Date:	2010-04-07 13:21:15 (UTC+0)
Publisher:	Asia University
Abstract:	Some of the wall-known fuzzy clustering algorithms are based on Euclidean distance function, which, can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were developed to detect non-spherical structural clusters. Both of GG and GK algorithms suffer from the singularity problem of covariance matrix and the effect of initial status. In this paper, a new Fuzzy C-Means algorithm based on Particle Swarm Optimization and Mahalanobis is distance without prior information (PSO-FCM-M) is proposed, to improve those limitations of GG and GK algorithms. And we point out that the PSO-FCM algorithm is a special case of PSO-FCM-M algorithm. The experimental results of two real data sets show that the performance of our proposed PSO-FCM-M algorithm is better than those of the FCM, GG, GK algorithms. © 2009 ISSN.
Relation:	International Journal of Innovative Computing, Information and Control 5(12):5033-5040
Appears in Collections:	[生物資訊與醫學工程學系 ] 期刊論文

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