• DocumentCode
    416845
  • Title

    A conditional clustering algorithm using self-organizing map

  • Author

    Tateyama, T. ; Kawata, S. ; Ohta, H.

  • Author_Institution
    Graduate Sch. of Eng., Tokyo Metropolitan Univ., Japan
  • Volume
    3
  • fYear
    2003
  • fDate
    4-6 Aug. 2003
  • Firstpage
    3259
  • Abstract
    A new clustering method using SOM is proposed. In our method, we can specify three parameters, the number of clusters, maximum and minimum number of elements in a cluster. The proposed method consists of three parts: SOM´s learning, setting of classification lines, and adjusting clusters. We applied this method to a plant layout planning problem and satisfactory results were obtained.
  • Keywords
    computer aided facilities layout; learning (artificial intelligence); planning (artificial intelligence); self-organising feature maps; SOM learning; classification line setting; conditional clustering algorithm; plant layout planning problem; self-organizing map;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SICE 2003 Annual Conference
  • Conference_Location
    Fukui, Japan
  • Print_ISBN
    0-7803-8352-4
  • Type

    conf

  • Filename
    1323910
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=416845