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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8787


    Title: Fuzzy possibility c-mean clustering algorithms based on complete Mahalanobis distances
    Authors: Liu, Hsiang-Chuan;Yih, Jeng-Ming;Wu, Der-Bang;Liu, Shin-Wu
    Contributors: Department of Bioinformatics
    Keywords: Algorithms;Cluster analysis;Color;Covariance matrix;Feature extraction;Flow of solids;Fuzzy clustering;Fuzzy rules;Fuzzy systems;Optical properties;Pattern recognition;Probability density function;Spheres;Wavelet analysis;Wavelet transforms;:CM;FCM;FCM-M;FPCM-CM;PCM-M
    Date: 2008
    Issue Date: 2010-04-08 20:06:06 (UTC+8)
    Publisher: Asia University
    Abstract: Two well known fuzzy partition clustering algorithms, FCM and FPCM are based on Euclidean distance function, which can only be used to detect spherical structural clusters. GK clustering algorithm and GG clustering algorithm, were developed to detect non-spherical structural clusters, but both of them fail to consider the relationships between cluster centers in the objective function, needing additional prior information.. In our previous studies, we developed two improved algorithms, FCM-M and FPCM-M, based on unsupervised Mahalanobis distance without any additional prior information. And FPCM-M is better than FCM-M, since the former has the more information about the typicalities than the later. In this paper, an improved new unsupervised algorithm, "fuzzy possibility c-mean based on complete Mahalanobis distance without any prior information (FPCM-CM)", is proposed. In our new algorithm, not only the local covariance matrix of each cluster but also the overall covariance matrix was considered. It can get more information and higher accuracy by considering the additional overall covariance matrix than FPCM-M. A real data set was applied to prove that the performance of the FPCM-CM algorithm is better than those of the traditional FCM and FPCM algorithm and our previous FCM-M.
    Relation: Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 1 :50-55
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

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