English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90069/105176 (86%)
Visitors : 6436564      Online Users : 528
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8797


    Title: Fuzzy possibility C-mean based on complete mahalanobis distance and separable criterion
    Authors: Liu, Hsiang-Chuan;Wu, Der-Bang;Yih, Jeng-Ming;Liu, Shin-Wu
    Contributors: Department of Bioinformatics
    Keywords: Color;Covariance matrix;Fuzzy clustering;Fuzzy rules;Fuzzy systems;Intelligent systems;Solenoids;Systems analysis;Data sets;Euclidean distance;FPCM-CM;Fuzzy partition;Improved algorithm;Mahalanobis distances;Non-Spherical;Prior information
    Date: 2008
    Issue Date: 2010-04-08 20:06:12 (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 need additional prior information. In our previous studies, we developed four improved algorithms, FCMM, FPCM-M, FCM-CM and FPCM-CM based on unsupervised Mahalanobis distance without any additional prior information. In first two algorithms, only the local covariance matrix of each cluster was considered, In last two algorithms, not only the local covariance matrix of each cluster but also the overall covariance matrix was considered, and FPCM-CM is the better one. In this paper, a more information about "separable criterion" is considered, and the further improved new algorithm, "fuzzy possibility c-mean based on complete Mahalanobis distance and separable criterion, (FPCM-CMS)" is proposed. It can get more information and higher accuracy by considering the additional separable criterion than FPCM-CM. A real data set was applied to prove that the performance of the FPCM-CMS algorithm is better than those of above six algorithms. © 2008 IEEE.
    Relation: Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008 1:89-94
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

    Files in This Item:

    File Description SizeFormat
    0KbUnknown241View/Open
    227.doc31KbMicrosoft Word376View/Open


    All items in ASIAIR are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback