• DocumentCode
    2017155
  • Title

    Non-metric neural clustering

  • Author

    Sato-Ilic, Mika

  • Author_Institution
    Inst. of Policy & Planning Sci., Tsukuba Univ., Ibaraki, Japan
  • Volume
    1
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    72
  • Abstract
    This paper presents an implementation of a non-metric clustering model using neural networks. The metric clustering model is correctly implemented by using the structure of neural networks based on the universal approximation theorem. However, it might be believed that the estimates as outputs contain little reliable information beyond their rank order. So, I discuss one implementation of a clustering model using a non-metric algorithm of neural networks
  • Keywords
    neural nets; pattern clustering; neural networks; nonmetric algorithm; nonmetric clustering model; nonmetric neural clustering; universal approximation theorem; Boundary conditions; Clustering algorithms; Fuzzy neural networks; Fuzzy sets; Humans; Neural networks; Reliability theory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on
  • Conference_Location
    Perth, WA
  • Print_ISBN
    0-7803-5871-6
  • Type

    conf

  • DOI
    10.1109/ICONIP.1999.843964
  • Filename
    843964