• DocumentCode
    393697
  • Title

    Linear fuzzy clustering based on least absolute deviations

  • Author

    Honda, K. ; Togo, N. ; Ichihashi, H.

  • Author_Institution
    Osaka Prefecture Univ., Japan
  • Volume
    4
  • fYear
    2002
  • fDate
    5-7 Aug. 2002
  • Firstpage
    2335
  • Abstract
    This paper proposes a technique of linear fuzzy clustering based on least absolute deviations. The least absolute deviations adopted in the method provide robust clustering results that are free from the influence of outliers. The simplicity of the proposed objective function makes it possible to handle missing values by simply ignoring only the missing coordinates.
  • Keywords
    fuzzy set theory; pattern clustering; least absolute deviations; linear fuzzy clustering; robust clustering; Clustering algorithms; Clustering methods; Eigenvalues and eigenfunctions; Frequency estimation; Fuzzy sets; Principal component analysis; Prototypes; Robustness; Scattering; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SICE 2002. Proceedings of the 41st SICE Annual Conference
  • Print_ISBN
    0-7803-7631-5
  • Type

    conf

  • DOI
    10.1109/SICE.2002.1195770
  • Filename
    1195770
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=393697