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Analytical and Numerical Evaluation of the Suppressed Fuzzy C-Means Algorithm

  • Conference paper
Modeling Decisions for Artificial Intelligence (MDAI 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5285))

Abstract

Suppressed fuzzy c-means (s-FCM) clustering was introduced in [Fan, J. L., Zhen, W. Z., Xie, W. X.: Suppressed fuzzy c-means clustering algorithm. Patt. Recogn. Lett. 24, 1607–1612 (2003)] with the intention of combining the higher speed of hard c-means (HCM) clustering with the better classification properties of fuzzy c-means (FCM) algorithm. They modified the FCM iteration to create a competition among clusters: lower degrees of memberships were diminished according to a previously set suppression rate, while the largest fuzzy membership grew by swallowing all the suppressed parts of the small ones. Suppressing the FCM algorithm was found successful in the terms of accuracy and working time, but the authors failed to answer a series of important questions. In this paper we clarify the view upon the optimality and the competitive behavior of s-FCM via analytical computations and numerical analysis.

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Author information

Authors and Affiliations

  1. Faculty of Technical and Human Science, Târgu-Mureş, Sapientia - Hungarian Science University of Transylvania, Romania

    László Szilágyi & Sándor M. Szilágyi

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

    László Szilágyi & Zoltán Benyó

Authors
  1. László Szilágyi
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  2. Sándor M. Szilágyi
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  3. Zoltán Benyó
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Editor information

Editors and Affiliations

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

    Vicenç Torra

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

    Yasuo Narukawa

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© 2008 Springer-Verlag Berlin Heidelberg

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Szilágyi, L., Szilágyi, S.M., Benyó, Z. (2008). Analytical and Numerical Evaluation of the Suppressed Fuzzy C-Means Algorithm. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2008. Lecture Notes in Computer Science(), vol 5285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88269-5_14

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  • DOI: https://doi.org/10.1007/978-3-540-88269-5_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88268-8

  • Online ISBN: 978-3-540-88269-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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