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Variables for Controlling Cluster Sizes on Fuzzy c-Means

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Modeling Decisions for Artificial Intelligence (MDAI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8234))

Abstract

The fuzzy c-means proposed by Dunn and Bezdek is one of the most popular methods of fuzzy clustering. Clusters obtained by the fuzzy c-means are in the Voronoi sets when crisp reallocation rule is applied. This means that a part of a larger cluster may be assigned to a smaller one when there are clusters of different sizes. Therefore, some methods using variables for controlling cluster sizes have been proposed. In this paper, we study their theoretical properties and compare them using numerical examples.

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References

  1. Dunn, J.C.: A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. Cybernetics and Systems 3(3), 32–57 (1973)

    MathSciNet  MATH  Google Scholar 

  2. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)

    Book  MATH  Google Scholar 

  3. Miyamoto, S., Kurosawa, N.: Controlling cluster volume sizes in fuzzy c-means clustering. In: SCIS and ISIS, Yokohama, Japan, pp. 1–4 (September 2004)

    Google Scholar 

  4. Yang, M.S.: On a class of fuzzy classification maximum likelihood procedures. Fuzzy Sets Syst. 57(3), 365–375 (1993)

    Article  MATH  Google Scholar 

  5. Ichihashi, H., Honda, K., Tani, N.: Gaussian mixture pdf approximation and fuzzy c-means clustering with entropy regularization. In: Proc. of the 4th Asian Fuzzy System Symposium, pp. 217–221 (2000)

    Google Scholar 

  6. Miyamoto, S., Ichihashi, H., Honda, K.: Algorithms for fuzzy clustering. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  7. Boots, B.N.: Weighting thiessen polygons. Economic Geography, 248–259 (1980)

    Google Scholar 

  8. Noordam, J., Van Den Broek, W., Buydens, L.: Multivariate image segmentation with cluster size insensitive fuzzy c-means. Chemometrics and Intelligent Laboratory Systems 64(1), 65–78 (2002)

    Article  Google Scholar 

  9. Lai, Y., Huang, P., Lin, P.: An integrity-based fuzzy c-means method resolving cluster size sensitivity problem. In: 2010 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 5, pp. 2712–2717. IEEE (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Master’s Program in Risk Engineering, University of Tsukuba, Ibaraki, 305-8573, Japan

    Yoshiyuki Komazaki

  2. Department of Risk Engineering, University of Tsukuba, Ibaraki, 305-8573, Japan

    Sadaaki Miyamoto

Authors
  1. Yoshiyuki Komazaki
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  2. Sadaaki Miyamoto
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Editor information

Editors and Affiliations

  1. IIIA-CSIC, Campüus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

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

    Yasuo Narukawa

  3. Departament d’Enginyeria de la Informació i de les Comunicacions, Universitat Autonoma de Barcelona, 08193, Bellaterra, Catalonia, Spain

    Guillermo Navarro-Arribas

  4. Internet Interdisciplinary Institute (IN3); Estudis d’Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018, Barcelona, Catalonia, Spain

    David Megías

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Komazaki, Y., Miyamoto, S. (2013). Variables for Controlling Cluster Sizes on Fuzzy c-Means. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Megías, D. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2013. Lecture Notes in Computer Science(), vol 8234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41550-0_17

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  • DOI: https://doi.org/10.1007/978-3-642-41550-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41549-4

  • Online ISBN: 978-3-642-41550-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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