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k-Medoids Clustering Based on Kernel Density Estimation and Jensen-Shannon Divergence

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Modeling Decisions for Artificial Intelligence (MDAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11676))

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Abstract

Several conventional clustering methods consider the squared \(L_2\)-norm which is calculated from objects coordinates. To extract meaningful clusters from a set of massive objects, it is required to calculate the dissimilarity from both objects coordinates and other features such as objects distribution. In this paper, JS-divergence based k-medoids (JSKMdd) is proposed as a novel method for clustering network data. In the proposed method, the dissimilarity that is based on objects coordinates and an object distribution is considered. The effectiveness of the proposed method is verified through numerical experiments with artificial datasets which consist non-linear clusters. The influence of the parameter in the proposed method is also described.

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Acknowledgments

This work was partly supported by JSPS KAKENHI Grant Numbers JP19K12146. This work was also partly supported by Telecommunications Advancement Foundation.

Author information

Authors and Affiliations

  1. Department of Informatics, School of Science and Engineering, Kindai University, 3-4-1 Kowakae, Higashiosaka, Osaka, 577-8502, Japan

    Yukihiro Hamasuna

  2. Graduate School of Science and Engineering, Kindai University, 3-4-1 Kowakae, Higashiosaka, Osaka, 577-8502, Japan

    Yuto Kingetsu & Shusuke Nakano

Authors
  1. Yukihiro Hamasuna
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  2. Yuto Kingetsu
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  3. Shusuke Nakano
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Corresponding author

Correspondence to Yukihiro Hamasuna .

Editor information

Editors and Affiliations

  1. Maynooth University, Maynooth, Ireland

    Vicenç Torra

  2. Tamagawa University, Machida, Tokyo, Japan

    Yasuo Narukawa

  3. University of Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  4. University of Milano-Bicocca, Milan, Italy

    Marco Viviani

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Hamasuna, Y., Kingetsu, Y., Nakano, S. (2019). k-Medoids Clustering Based on Kernel Density Estimation and Jensen-Shannon Divergence. In: Torra, V., Narukawa, Y., Pasi, G., Viviani, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2019. Lecture Notes in Computer Science(), vol 11676. Springer, Cham. https://doi.org/10.1007/978-3-030-26773-5_24

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  • DOI: https://doi.org/10.1007/978-3-030-26773-5_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26772-8

  • Online ISBN: 978-3-030-26773-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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