• DocumentCode
    3263399
  • Title

    Possibilistic c-regression models clustering algorithm

  • Author

    Chung-Chun Kung ; Hong-Chi Ku ; Jui-Yiao Su

  • Author_Institution
    Dept. of Electr. Eng., Tatung Univ., Taipei, Taiwan
  • fYear
    2013
  • fDate
    4-6 July 2013
  • Firstpage
    297
  • Lastpage
    302
  • Abstract
    The purpose of this paper is to apply the possibilistic c-means (PCM) clustering algorithm to the fuzzy c-regression models (FCRM) clustering algorithm and propose a new clustering algorithm named possibilistic c-regression models (PCRM). The PCRM clustering algorithms relaxes the column sum constrain result in each cluster, it will alleviate the noisy data effectively. Finally, the simulation examples are provided to demonstrate the effectiveness of the PCRM clustering algorithm.
  • Keywords
    fuzzy set theory; pattern clustering; regression analysis; FCRM clustering algorithm; PCM clustering algorithm; PCRM clustering algorithms; column sum constrain; fuzzy c-regression model clustering algorithm; noisy data; possibilistic c-regression models clustering algorithm; Clustering algorithms; Conferences; Equations; Noise; Noise measurement; Partitioning algorithms; Signal processing algorithms; fuzzy c-regression models (FCRM); fuzzy clustering; possibilistic c-means (PCM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Science and Engineering (ICSSE), 2013 International Conference on
  • Conference_Location
    Budapest
  • ISSN
    2325-0909
  • Print_ISBN
    978-1-4799-0007-7
  • Type

    conf

  • DOI
    10.1109/ICSSE.2013.6614679
  • Filename
    6614679