• DocumentCode
    3573265
  • Title

    The graded possibilistic clustering model

  • Author

    Masulli, Francesco ; Rovetta, Stefano

  • Author_Institution
    Nat. Inst. for the Phys. of Matter, INFM, Italy
  • Volume
    1
  • fYear
    2003
  • Firstpage
    791
  • Abstract
    This paper presents the graded possibilistic model. After reviewing some clustering algorithms derived from c-Means, we provide a unified perspective on these clustering algorithms, focused on the memberships rather than on the cost function. Then the concept of graded possibility is introduced. This is a partially possibilistic version of the fuzzy clustering model, as compared to Krishnapuram and Keller´s possibilistic clustering. We outline a basic graded possibilistic clustering algorithm and highlight the different properties attainable by means of experimental demonstrations.
  • Keywords
    fuzzy set theory; pattern clustering; self-organising feature maps; vector quantisation; Keller possibilistic clustering; Krishnapuram possibilistic clustering; c-Means; clustering algorithms; cost function; fuzzy clustering model; graded possibilistic clustering model; partially possibilistic version; Annealing; Clustering algorithms; Computer science; Cost function; Electronic mail; Membership renewal; Neural networks; Partitioning algorithms; Physics computing; Self organizing feature maps;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2003. Proceedings of the International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7898-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2003.1223483
  • Filename
    1223483