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Additive Clustering Model and Its Generalization

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Data Science, Classification, and Related Methods

Summary

ADCLUS (ADditive CLUStering) is known as a clustering model which is designated for the purpose of finding the structure of the similarity data. The aim of this paper is to generalize this model from several points of view. The fast point of view is to extend the degree of belongingness of the objects to the continuous value in the interval [0,1], namely to an additive fuzzy clustering model, because the combinatorial optimization is inevitable in the algorithm for ADCLUS. The second point of view is to generalize the model for an asymmetric similarity data. And the third point of view is that we introduce the aggregation operator in the model to represent the degree of simultaneous belongingness of the objects to each cluster.

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Author information

Authors and Affiliations

  1. Institute of Policy and Planning Science, University of Tsukuba, Tenodai 1-1-1, Tsukuba, 305, Japan

    Mika Sato

  2. Department of Information and Management Science, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo, 060, Japan

    Yoshiharu Sato

Authors
  1. Mika Sato
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  2. Yoshiharu Sato
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Editor information

Editors and Affiliations

  1. The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106, Japan

    Chikio Hayashi , Noboru Ohsumi  & Yasumasa Baba ,  & 

  2. School of Management, Science University of Tokyo, 500 Shimokiyoku, Kuki, Saitama 346, Japan

    Keiji Yajima

  3. Institut für Statistik, Rheinisch-Westfälische Technische Hochschule (RWTH), D-52056, Aachen, Germany

    Hans-Hermann Bock

  4. Faculty of Environmental Science & Technology, Okayama University, 2-1-1 Tsushima-naka, Okayama 700, Japan

    Yutaka Tanaka

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© 1998 Springer Japan

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Sato, M., Sato, Y. (1998). Additive Clustering Model and Its Generalization. In: Hayashi, C., Yajima, K., Bock, HH., Ohsumi, N., Tanaka, Y., Baba, Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65950-1_34

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  • DOI: https://doi.org/10.1007/978-4-431-65950-1_34

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-70208-5

  • Online ISBN: 978-4-431-65950-1

  • eBook Packages: Springer Book Archive

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