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On Kernelization for a Maximizing Model of Bezdek-Like Spherical Fuzzy c-Means Clustering

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Modeling Decisions for Artificial Intelligence (MDAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8825))

Abstract

In this study, we propose three modifications for a maximizing model of spherical Bezdek-type fuzzy c-means clustering (msbFCM). First, we kernelize msbFCM (K-msbFCM). The original msbFCM can only be applied to objects on the first quadrant of the unit hypersphere, whereas its kernelized form can be applied to a wider class of objects. The second modification is a spectral clustering approach to K-msbFCM using a certain assumption. This approach solves the local convergence problem in the original algorithm. The third modification is to construct a model providing the exact solution of the spectral clustering approach. Numerical examples demonstrate that the proposed methods can produce good results for clusters with nonlinear borders when an adequate parameter value is selected.

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Author information

Authors and Affiliations

  1. Shibaura Institute of Technology, Koto, Tokyo, 135-8548, Japan

    Yuchi Kanzawa

Authors
  1. Yuchi Kanzawa
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Editors and Affiliations

  1. CSIC, Consejo Superior de Investigaciones Cientificas, 08193, Bellaterra, Spain

    Vicenç Torra

  2. Toho Gakuen, 186-0004, Tokyo, Japan

    Yasuo Narukawa

  3. Faculty of Engineering, Information and Systems, University of Tsukuba, 305-8573, Tsukuba, Ibaraki, Japan

    Yasunori Endo

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Kanzawa, Y. (2014). On Kernelization for a Maximizing Model of Bezdek-Like Spherical Fuzzy c-Means Clustering. In: Torra, V., Narukawa, Y., Endo, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2014. Lecture Notes in Computer Science(), vol 8825. Springer, Cham. https://doi.org/10.1007/978-3-319-12054-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-12054-6_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12053-9

  • Online ISBN: 978-3-319-12054-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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