• DocumentCode
    2786623
  • Title

    Algorithms for k-means clustering problem with balancing constraint

  • Author

    Shouqiang, Wang ; Zengxiao, Chi ; Sheng, Zhan

  • Author_Institution
    Dept. of Inf. Eng., Shandong Jiaotong Univ., Jinan, China
  • fYear
    2009
  • fDate
    17-19 June 2009
  • Firstpage
    3967
  • Lastpage
    3972
  • Abstract
    k-means clustering has been widely applied in the field of Machine Learning and Pattern Recognition. This paper discussed the algorithm of its sub problem which requires that each divided subset size must have at least some given value. Firstly, given k centers, this paper presented an algorithm that assigned each point to one of the centers and proved that the solution value is minimized. Secondly, a 2-approximate algorithm is also presented by the sample technique. At last some UCI datasets were selected to verify our algorithm.
  • Keywords
    approximation theory; learning (artificial intelligence); pattern clustering; 2-approximate algorithm; K-means clustering problem algorithms; balancing constraint; machine learning; pattern recognition; Clustering algorithms; Machine learning; Machine learning algorithms; Pattern recognition; Algorithm; Balancing Constraint; Clustering; k-means;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference, 2009. CCDC '09. Chinese
  • Conference_Location
    Guilin
  • Print_ISBN
    978-1-4244-2722-2
  • Electronic_ISBN
    978-1-4244-2723-9
  • Type

    conf

  • DOI
    10.1109/CCDC.2009.5192108
  • Filename
    5192108