• DocumentCode
    3029508
  • Title

    Fixed point relational fuzzy clustering

  • Author

    Brouwer, Roelof K.

  • Author_Institution
    Dept. of Comput. Sci., Thompson Rivers Univ., Kamloops, BC
  • fYear
    2009
  • fDate
    10-12 Feb. 2009
  • Firstpage
    115
  • Lastpage
    120
  • Abstract
    The proposed relational fuzzy clustering method called FRFP (fuzzy relational fixed point) is not based on minimizing an objective function, as in traditional methods, but rather on determining a fixed point of a function of the desired membership matrix with the proximity matrix as parameter. The proposed method is compared to other relational clustering methods including NERFCM, Rouben´s method and Windhams AP method. A clustering quality index is calculated for doing the comparison. using various proximity matrices as input. Simulations show the method to be very effective and less computationally expensive than other fuzzy relational data clustering methods.
  • Keywords
    fuzzy set theory; pattern clustering; clustering quality index; fixed point relational fuzzy clustering; fuzzy relational fixed point; membership matrix; proximity matrix; Clustering algorithms; Clustering methods; Computational modeling; Fuzzy sets; Knowledge acquisition; Partitioning algorithms; Prototypes; Rivers; Robots; Robustness; Fuzzy clustering; cluster quality; clustering quality; multidimensional scaling; optimization; prototype-less fuzzy clustering; relational fuzzy clustering; visual representation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Autonomous Robots and Agents, 2009. ICARA 2009. 4th International Conference on
  • Conference_Location
    Wellington
  • Print_ISBN
    978-1-4244-2712-3
  • Electronic_ISBN
    978-1-4244-2713-0
  • Type

    conf

  • DOI
    10.1109/ICARA.2000.4803942
  • Filename
    4803942