• DocumentCode
    2446259
  • Title

    Genetic fuzzy clustering

  • Author

    Hall, L.O. ; Bezdek, J.C. ; Boggavarpu, S. ; Bensaid, A.

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA
  • fYear
    1994
  • fDate
    18-21 Dec 1994
  • Firstpage
    411
  • Lastpage
    415
  • Abstract
    This paper describes a genetic guided fuzzy clustering algorithm. The fuzzy-c-means functional Jm is used as the fitness function. In two domains the approach is shown to avoid some higher values of Jm to which the fuzzy-c-means algorithm will converge under some initializations. Hence, the genetic guided approach shows promise as a clustering tool
  • Keywords
    fuzzy logic; genetic algorithms; fitness function; fuzzy-c-means functional; genetic fuzzy clustering; Clustering algorithms; Computer science; Genetic algorithms; Genetic engineering; Image converters; Image segmentation; Iterative algorithms; Minimization methods; Optimization methods; Prototypes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information Processing Society Biannual Conference, 1994. Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic,
  • Conference_Location
    San Antonio, TX
  • Print_ISBN
    0-7803-2125-1
  • Type

    conf

  • DOI
    10.1109/IJCF.1994.375077
  • Filename
    375077
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2446259