• DocumentCode
    2416623
  • Title

    L1-Norm based Fuzzy Clustering for Data with Tolerance

  • Author

    Endo, Yasunori ; Murata, Ryuichi ; Toyoda, Hiromi ; Miyamoto, Sadaaki

  • Author_Institution
    Univ. of Tsukuba, Tsukuba
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    770
  • Lastpage
    777
  • Abstract
    In this paper, the clustering algorithms for data with tolerance are constructed based on L1-norm and the effectiveness is verified through numerical examples. First, two objective functions, which are based on SFCM-T and EFCM-T respectively, is defined. It is more complex to calculate exact solutions of these functions theoretically in the L1-norm space than the L1-norm space (Euclidean space) so that two methods to obtain the solutions are proposed. Next, two kinds of clustering algorithms based on L1-norm are proposed using the two methods to obtain the exact solutions. Last, the effectiveness of the proposed algorithms is verified through the numerical examples of an artificial data set and the Iris data set.
  • Keywords
    data analysis; fuzzy set theory; pattern clustering; Euclidean space; L1-norm based fuzzy data clustering algorithm; Clustering algorithms; Fuzzy systems; Piecewise linear techniques; Sorting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2006 IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9488-7
  • Type

    conf

  • DOI
    10.1109/FUZZY.2006.1681797
  • Filename
    1681797
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2416623