• DocumentCode
    2554141
  • Title

    An improved K-means clustering algorithm

  • Author

    Zhu, Jian ; Wang, Hanshi

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Beijing Inst. of Technol., Beijing, China
  • fYear
    2010
  • fDate
    16-18 April 2010
  • Firstpage
    190
  • Lastpage
    192
  • Abstract
    That traditional K-mean algorithm is a widely used clustering algorithm, with a wide application. In light of the disadvantage of K-mean algorithm, improvement is made to the traditional K-mean algorithm, a k value learning algorithm is proposed. Using genetic algorithm to optimize the K value, and improve clustering performance.
  • Keywords
    genetic algorithms; pattern clustering; genetic algorithm; k value learning algorithm; k-means clustering algorithm; Application software; Clustering algorithms; Computer science; Data compression; Data mining; Euclidean distance; Genetic algorithms; Modeling; Neural networks; Radial basis function networks; Clustering algorithm; K-mean value; genetic algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-5263-7
  • Electronic_ISBN
    978-1-4244-5265-1
  • Type

    conf

  • DOI
    10.1109/ICIME.2010.5478087
  • Filename
    5478087
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2554141