• DocumentCode
    3249542
  • Title

    Robust clustering algorithm for suppression of outliers [data classification applications]

  • Author

    Lam, Benson S Y ; Yan, Hong

  • Author_Institution
    Dept. of Comput. Eng. & Inf. Technol., City Univ. of Hong Kong, Kowloon, China
  • fYear
    2004
  • fDate
    20-22 Oct. 2004
  • Firstpage
    691
  • Lastpage
    694
  • Abstract
    The fuzzy c-means clustering algorithm has been widely used in many data classification problems. However, the performance of this algorithm is easily degraded if an outlier is present. To solve this problem, we introduce the modified l2 norm in the clustering formulation, which can overcome the influence of outliers effectively. Our experiments show that the proposed method is able to generate accurate results even if outliers are present.
  • Keywords
    fuzzy logic; signal classification; data classification; fuzzy c-means clustering algorithm; l2 norm; outlier suppression; Clustering algorithms; Degradation; Equations; Information technology; Iterative algorithms; Partitioning algorithms; Robustness; Soil; Speech processing; Tiles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on
  • Print_ISBN
    0-7803-8687-6
  • Type

    conf

  • DOI
    10.1109/ISIMP.2004.1434158
  • Filename
    1434158
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3249542