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Optimal Clustering with Twofold Memberships

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Modeling Decisions for Artificial Intelligence (MDAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11144))

Abstract

This paper proposes two clustering algorithms of twofold memberships for each cluster. One uses a membership similar to that in K-means, while another membership is defined for a core of a cluster, which is compared to the lower approximation of a cluster in rough K-means. Two ideas for the lower approximation are proposed in this paper: one uses a neighborhood of a cluster boundary and another uses a simple circle from a cluster center. By using the two memberships, two alternate optimization algorithms are proposed. Numerical examples show the effectiveness of the proposed algorithms.

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Notes

  1. 1.

    https://clusteval.sdu.dk/1/datasets.

  2. 2.

    http://sci2s.ugr.es/keel/category.php?cat=clas.

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Acknowledgment

This paper is based upon work supported in part by the Air Force Office of Scientific Research/Asian Office of Aerospace Research and Development (AFOSR/AOARD) under award number FA2386-17-1-4046.

Author information

Authors and Affiliations

  1. University of Tsukuba, Tsukuba, Japan

    Sadaaki Miyamoto, Jong Moon Choi & Yasunori Endo

  2. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Van Nam Huynh

Authors
  1. Sadaaki Miyamoto
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  2. Jong Moon Choi
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  3. Yasunori Endo
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  4. Van Nam Huynh
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Corresponding author

Correspondence to Sadaaki Miyamoto .

Editor information

Editors and Affiliations

  1. Maynooth University, Maynooth, Ireland

    Vicenç Torra

  2. Department Management Science, Tamagawa University, Tokyo, Japan

    Yasuo Narukawa

  3. University of the Balearic Islands, Palma de Mallorca, Spain

    Isabel Aguiló

  4. University of the Balearic Islands, Palma de Mallorca, Baleares, Spain

    Manuel González-Hidalgo

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Miyamoto, S., Choi, J.M., Endo, Y., Huynh, V.N. (2018). Optimal Clustering with Twofold Memberships. In: Torra, V., Narukawa, Y., Aguiló, I., González-Hidalgo, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2018. Lecture Notes in Computer Science(), vol 11144. Springer, Cham. https://doi.org/10.1007/978-3-030-00202-2_18

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  • DOI: https://doi.org/10.1007/978-3-030-00202-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00201-5

  • Online ISBN: 978-3-030-00202-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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