• DocumentCode
    2821657
  • Title

    Fuzzy Clustering with Improved Artificial Fish Swarm Algorithm

  • Author

    He, Si ; Belacel, Nabil ; Hamam, Habib ; Bouslimani, Yassine

  • Author_Institution
    Electr. Eng. Dept., Univ. de Moncton, Moncton, NB, Canada
  • Volume
    2
  • fYear
    2009
  • fDate
    24-26 April 2009
  • Firstpage
    317
  • Lastpage
    321
  • Abstract
    This paper applies the artificial fish swarm algorithm (AFSA) to fuzzy clustering. An improved AFSA with adaptive visual and adaptive step is proposed. AFSA enhances the performance of the fuzzy C-means (FCM) algorithm. A computational experiment shows that AFSA improved FCM out performs both the conventional FCM algorithm and the genetic algorithm (GA) improved FCM.
  • Keywords
    fuzzy set theory; genetic algorithms; pattern clustering; ATSA; FCM; GA; artificial fish swarm algorithm; fuzzy C-means algorithm; fuzzy clustering; genetic algorithm; Artificial intelligence; Clustering algorithms; Clustering methods; Councils; Genetic algorithms; Helium; Information technology; Marine animals; Particle swarm optimization; Pattern recognition; Artificial Fish Swarm Algorithm; Fuzzy Clustering; fuzzy C-Means;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on
  • Conference_Location
    Sanya, Hainan
  • Print_ISBN
    978-0-7695-3605-7
  • Type

    conf

  • DOI
    10.1109/CSO.2009.367
  • Filename
    5193959
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2821657