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The Most Random Partition of a Finite Set and its Application to Classification

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Data Science, Classification, and Related Methods

  • 2012 Accesses

Summary

The notion of ‘the most random partition of a finite set’ is defined and proposed as a null model in classification. A Hamming-type distance between two independent and most random partitions is used for justifying its randomness, and is used for testing this null hypothesis. The probability distribution of the distance is studied for the latter purpose.

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Author information

Authors and Affiliations

  1. Takachiho University, 2-19-1 Ohmiya, Suginaki-ku, Tokyo 168, Japan

    Masaaki Sibuya

Authors
  1. Masaaki Sibuya
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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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Sibuya, M. (1998). The Most Random Partition of a Finite Set and its Application to Classification. 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_25

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  • DOI: https://doi.org/10.1007/978-4-431-65950-1_25

  • 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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