STRUCTURAL MODEL OF SIMILARITY FOR FUZZY CLUSTERING

    • Sato Mika
    • Division of Information Engineering, Hokkaido University

抄録

As a generalization of the additive clustering model (Shepard, R. N. and Arabie, P. (1979)), we discuss the following three additive fuzzy clustering models: a simple additive fuzzy clustering model, an overlapping fuzzy clustering model and a fuzzy clustering model for ordinal scaled similarity. The essential merits of fuzzy clustering models are 1) the amounts of computations for the identification of the models are much fewer than a hard clustering model and 2) a fewer number of clusters is needed to get a suitable fitness. These fuzzy clustering models are extended to the model for asymmetric similarity. In this model, the concept of the similarity among clusters is introduced. The crucial assumption of this model is that the asymmetry of the similarity between the pair of objects is caused by the asymmetric similarity among clusters. The validity of this model is shown by some examples.

収録刊行物

Journal of the Japanese Society of Computational Statistics   [巻号一覧]

Journal of the Japanese Society of Computational Statistics 7(1), 27-46, 1994-12  [この号の目次]

日本計算機統計学会

被引用文献:  2件

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各種コード

  • NII論文ID(NAID) :
    110001235609
  • NII書誌ID(NCID) :
    AA10823693
  • 本文言語コード :
    ENG
  • 資料種別 :
    雑誌論文
  • ISSN :
    09152350
  • 収録DB :
    CJP引用  NII-ELS