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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8785


    Title: Fuzzy C-mean algorithm based on "complete" mahalanobis distances
    Authors: Liu, Hsiang-Chuan;Yih, Jeng-Ming;Wu, Der-Bang;Liu, Shin-W.U.
    Contributors: Department of Bioinformatics
    Keywords: Color;Control theory;Covariance matrix;Cybernetics;Diesel engines;Fuzzy clustering;Fuzzy rules;Fuzzy systems;Learning systems;Optical properties;Robot learning;Spheres;FCM;FCM-CM;FCM-M;GG;GK;Mahalanobis distances
    Date: 2008
    Issue Date: 2010-04-08 20:06:05 (UTC+8)
    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: Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC 6 :3569-3574
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

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