• DocumentCode
    539531
  • Title

    A Modified k-means Algorithm for Clustering Problem with Balancing Constraints

  • Author

    Yuepeng, Sun ; Min, Liu ; Cheng, Wu

  • Author_Institution
    Dept. of Autom., Tsinghua Univ., Beijing, China
  • Volume
    1
  • fYear
    2011
  • fDate
    6-7 Jan. 2011
  • Firstpage
    127
  • Lastpage
    130
  • Abstract
    A clustering problem with balancing constraints is studied in this paper, which means that the sample number in each cluster has to be at least pre-given value. A modified k-means clustering algorithm is proposed, which adopt the proposed heuristic cluster assignment algorithm to deal with the balancing constraints. Numerical computation shows that the proposed algorithm can deal with the balancing constraints and lead to the improvement of the clustering objective.
  • Keywords
    pattern clustering; unsupervised learning; balancing constraints; clustering problem; modified k-means algorithm; Algorithm design and analysis; Approximation algorithms; Clustering algorithms; Computed tomography; Databases; Heuristic algorithms; Silicon; Balancing Constraints; Clustering Algorithm; k-means;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on
  • Conference_Location
    Shangshai
  • Print_ISBN
    978-1-4244-9010-3
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2011.37
  • Filename
    5720738
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=539531