• DocumentCode
    3398852
  • Title

    Relational Gustafson Kessel Clustering Using Medoids and Triangulation

  • Author

    Runkler, Thomas A.

  • Author_Institution
    Dept. of Neural Comput., Siemens AG, Munich
  • fYear
    2005
  • fDate
    25-25 May 2005
  • Firstpage
    73
  • Lastpage
    78
  • Abstract
    This paper deals with clustering relational data that can be (at least approximately) represented by object data with ellipsoidal clusters. Conventional relational clustering models such as relational fuzzy c-means or relational fuzzy c-medoids produce bad results for this family of relational data, because they do not consider the cluster shape. In this paper, we develop a Gustafson Kessel model where the cluster centers are medoids. For relational data, the scatter matrices and the matrix distances are locally computed using triangulation. The resulting RGKMdd algorithm produces very good results for the family of relational data specified above
  • Keywords
    fuzzy set theory; pattern clustering; relational databases; Gustafson-Kessel clustering; ellipsoidal clusters; matrix distances; relational data clustering; relational fuzzy c-means; relational fuzzy c-medoids; scatter matrix; triangulation; Clustering algorithms; Communications technology; Computational complexity; Fuzzy sets; Fuzzy systems; Iterative algorithms; Scattering; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on
  • Conference_Location
    Reno, NV
  • Print_ISBN
    0-7803-9159-4
  • Type

    conf

  • DOI
    10.1109/FUZZY.2005.1452371
  • Filename
    1452371