ASIA unversity:Item 310904400/8646
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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8646


    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:[Department of Biomedical informatics  ] Journal Article

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