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A Unified Theory of Fuzzy c-Means Clustering Models with Improved Partition

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Modeling Decisions for Artificial Intelligence (MDAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9321))

Abstract

This paper attempts to unify the theory of a certain class of modified variants and another class of manipulated versions of the fuzzy c-means algorithm. Starting from the objective function of the so-called fuzzy c-means algorithm with generalized improved partition (GIFP-FCM), and defining its rewarding term in a more flexible way, we obtain a unified algorithm that can model all algorithm variants in question including the wide family of suppressed and generalized suppressed FCM. Numerical tests were carried out to provide a comparison of the modeled algorithms in terms of accuracy and cluster size insensitivity. The suppression of the probabilistic fuzzy partition obtained at high values of the fuzzy exponent m proved the most effective.

Research supported by the Hungarian National Research Funds (OTKA), Project no. PD103921.

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Authors and Affiliations

  1. Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary

    László Szilágyi

  2. Faculty of Technical and Human Science of Tîrgu-Mureş, Sapientia - Hungarian Science University of Transylvania, Tîrgu-Mureş, Romania

    László Szilágyi

  3. Canterbury University of Christchurch, Christchurch, New Zealand

    László Szilágyi

Authors
  1. László Szilágyi
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Correspondence to László Szilágyi .

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Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Vicenc Torra

  2. Toho Gakuen, Tokyo, Japan

    Torra Narukawa

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Szilágyi, L. (2015). A Unified Theory of Fuzzy c-Means Clustering Models with Improved Partition. In: Torra, V., Narukawa, T. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2015. Lecture Notes in Computer Science(), vol 9321. Springer, Cham. https://doi.org/10.1007/978-3-319-23240-9_11

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  • DOI: https://doi.org/10.1007/978-3-319-23240-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23239-3

  • Online ISBN: 978-3-319-23240-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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