• DocumentCode
    598826
  • Title

    DS_CABOSFV clustering algorithm for high dimensional data stream

  • Author

    Jing Pan

  • Author_Institution
    Dongling School of Economics Management, University of Science and Technology Beijing, China
  • fYear
    2012
  • fDate
    21-24 Aug. 2012
  • Firstpage
    16
  • Lastpage
    19
  • Abstract
    Data stream clustering has become a hot research issue. The high-dimensional data stream clustering is a difficult problem for the data stream mining because the large volumes of data arriving in a stream make most traditional algorithms too inefficient. In this paper, DS_CABOSFV, a high-dimensional data stream clustering algorithm based on CABOSFV algorithm is presented. Our empirical tests show that DS_CABOSFV has low computational complexity and good efficiency for high-dimensional data stream clustering.
  • Keywords
    clustering; high dimensionality; stream data mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Awareness Science and Technology (iCAST), 2012 4th International Conference on
  • Conference_Location
    Seoul, Korea (South)
  • Print_ISBN
    978-1-4673-2111-2
  • Electronic_ISBN
    978-1-4673-2110-5
  • Type

    conf

  • DOI
    10.1109/iCAwST.2012.6469582
  • Filename
    6469582