• DocumentCode
    1563941
  • Title

    A new weighting fuzzy c-means algorithm

  • Author

    Yu, Jian ; Houkuan Huang

  • Author_Institution
    Dept. of Comput. Sci. & Technol., Northern Jiao Tong Univ., Beijing, China
  • Volume
    2
  • fYear
    2003
  • Firstpage
    896
  • Abstract
    It is always supposed that each cluster has almost equal number of points when carrying out the FCM. However, this assumption may not hold in practice. In this paper, we propose a novel fuzzy c-means algorithm based on the definition of generalized mean, weighting fuzzy c-means algorithms (WFCM). Noticing that the Gath-Geva fuzzy clustering algorithm considers the size of the clusters, we compare the performance between the WFCM and the Gath-Geva fuzzy clustering algorithms by numerical experiments. Moreover, we offer a theoretical threshold to choose an appropriate weighting exponent in the WFCM, which is also valid for the FCM, the numeric experiments verify such conclusion.
  • Keywords
    fuzzy logic; fuzzy set theory; generalisation (artificial intelligence); pattern clustering; Gath-Geva fuzzy clustering algorithm; numerical experiments; performance evaluation; weighting exponent; weighting fuzzy c means algorithm; Algorithm design and analysis; Clustering algorithms; Computer science; Equations; Image analysis; Image databases; Image processing; Image recognition; Process design; Spatial databases;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on
  • Print_ISBN
    0-7803-7810-5
  • Type

    conf

  • DOI
    10.1109/FUZZ.2003.1206550
  • Filename
    1206550