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Application of the Fuzzy-Possibilistic Product Partition in Elliptic Shell Clustering

  • Conference paper
Modeling Decisions for Artificial Intelligence (MDAI 2014)

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

Abstract

Creating accurate and robust clustering models is utmost important in pattern recognition. This paper introduces an elliptic shell clustering model aiming at accurate detection of ellipsoids in the presence of outlier data. The proposed fuzzy-possibilistic product partition c-elliptical shell algorithm (FP3CES) combines the probabilistic and possibilistic partitions in a qualitatively different way from previous, similar algorithms. The novel mixture partition is able to suppress the influence of extreme outlier data, which gives it net superiority in terms of robustness and accuracy compared to previous algorithms, fact supported by cluster validity indices.

Research supported by the Hungarian National Research Funds (OTKA), Project no. PD103921 and the MTA János Bolyai Fellowship Program.

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Author information

Authors and Affiliations

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

    László Szilágyi

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

    László Szilágyi & Zsuzsa Réka Varga

  3. Dept. of Mathematics - Informatics, Petru Maior University of Tîrgu-Mureş, Romania

    Sándor Miklós Szilágyi

Authors
  1. László Szilágyi
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  2. Zsuzsa Réka Varga
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  3. Sándor Miklós Szilágyi
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Editor information

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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Szilágyi, L., Varga, Z.R., Szilágyi, S.M. (2014). Application of the Fuzzy-Possibilistic Product Partition in Elliptic Shell 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_14

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  • DOI: https://doi.org/10.1007/978-3-319-12054-6_14

  • 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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