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On Fuzzy c-Means for Data with Tolerance

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Modeling Decisions for Artificial Intelligence (MDAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3885))

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Abstract

This paper presents two new clustering algorithms which are based on the entropy regularized fuzzy c-means and can treat data with some errors. First, the tolerance which means the permissible range of the error is introduced into optimization problems which relate with clustering, and the tolerance is formulated. Next, the problems are solved using Kuhn-Tucker conditions. Last, the algorithms are constructed based on the results of solving the problems.

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References

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Authors and Affiliations

  1. Graduate School of Systems and Information Engineering, University of Tsukuba Ibaraki, 305-8573, Japan

    Ryuichi Murata & Hideyuki Haruyama

  2. Department of Risk Engineering Faculty of Systems and Information Engineering, University of Tsukuba Ibaraki, 305-8573, Japan

    Yasunori Endo & Sadaaki Miyamoto

Authors
  1. Ryuichi Murata
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  2. Yasunori Endo
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  3. Hideyuki Haruyama
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  4. Sadaaki Miyamoto
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Editor information

Editors and Affiliations

  1. IIIA, Artificial Intelligence Research Institute CSIC, Spanish National Research Council, Campus UAB s/n, Catalonia, 08193, Bellaterra, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10 Naka, 186-0004, Tokyo, Kunitachi, Japan

    Yasuo Narukawa

  3. Department of Computer Science and Mathematics Intelligent Technologies for Advanced Knowledge Acquisition Research Group,, University Rovira i Virgili, Av. Països Catalans, 26., 43007, Tarragona, Catalonia, Spain

    Aïda Valls

  4. Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007, Tarragona, Catalonia, Spain

    Josep Domingo-Ferrer

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© 2006 Springer-Verlag Berlin Heidelberg

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Murata, R., Endo, Y., Haruyama, H., Miyamoto, S. (2006). On Fuzzy c-Means for Data with Tolerance. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2006. Lecture Notes in Computer Science(), vol 3885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11681960_34

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  • DOI: https://doi.org/10.1007/11681960_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32780-6

  • Online ISBN: 978-3-540-32781-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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