• DocumentCode
    1588035
  • Title

    K-means clustering analysis based on immune genetic algorithm

  • Author

    Cai, Lecai ; Yao, Xingyan ; He, Zhiyong ; Liang, Xingjian

  • Author_Institution
    Inst. of Comput. Applic., Sichuan Univ. of Sci. & Eng., Zigong, China
  • fYear
    2010
  • Firstpage
    413
  • Lastpage
    418
  • Abstract
    A novel algorithm, the k-means clustering algorithm based on immune genetic algorithm (KMCIGA) is put forward. To improve the Genetic operators, the conception of concentration in the immune algorithm and the dynamic chromosome coding are used. Strategies and methods of selecting vaccines and constructing an immune operator are also given. KMCIGA is illustrated to be obviously better than the traditional K-Means clustering algorithm in clustering division, and can improve the searching ability and adaptability, greatly increase the converging speed.
  • Keywords
    biology computing; genetic algorithms; pattern clustering; KMCIGA; dynamic chromosome coding; immune genetic algorithm; k-means clustering analysis; Algorithm design and analysis; Biological cells; Classification algorithms; Clustering algorithms; Encoding; Gallium; Genetic algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    World Automation Congress (WAC), 2010
  • Conference_Location
    Kobe
  • ISSN
    2154-4824
  • Print_ISBN
    978-1-4244-9673-0
  • Electronic_ISBN
    2154-4824
  • Type

    conf

  • Filename
    5665368