• DocumentCode
    1185390
  • Title

    A New Convergence Proof of Fuzzy c-Means

  • Author

    Gröll, Lutz ; Jäkel, Jens

  • Author_Institution
    Inst. for Appl. Comput. Sci., Forschungszentrum Karlsruhe
  • Volume
    13
  • Issue
    5
  • fYear
    2005
  • Firstpage
    717
  • Lastpage
    720
  • Abstract
    In this letter, we give a new, more direct derivation of the convergence properties of the fuzzy c-means (FCM) algorithm, using the equivalence between the original and reduced FCM criterion. From the point of view of the reduced criterion, the FCM algorithm is simply a steepest descent algorithm with variable steplength. We prove that steplength adjustment follows from the majorization principle for steplength. By applying the majorization principle we give a straightforward proof of global convergence. Further convergence properties follow immediately using known results of optimization theory
  • Keywords
    convergence; optimisation; pattern clustering; convergence proof; fuzzy c-means algorithm; optimization theory; reduced criterion; steepest descent algorithm; steplength adjustment; Closed-form solution; Clustering algorithms; Computer science; Convergence; Equations; Fuzzy sets; Optimization methods; Convergence; fuzzy c-means (FCM); fuzzy clustering; majorization principle for steplength;
  • fLanguage
    English
  • Journal_Title
    Fuzzy Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1063-6706
  • Type

    jour

  • DOI
    10.1109/TFUZZ.2005.856560
  • Filename
    1516160