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Fuzzy Sets, Rough Sets, Multisets and Clustering pp 101–121Cite as

Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 671))

Abstract

One of the main challenges in the field of clustering is creating algorithms that are both accurate and robust. The fuzzy-possibilistic product partition c-means clustering algorithm was introduced with the main goal of producing accurate partitions in the presence of outlier data. This chapter presents several clustering algorithms based on the fuzzy-possibilistic product partition, specialized for the detection of clusters having various shapes including spherical and ellipsoidal shells. The advantages of applying the fuzzy-possibilistic product partition are presented in comparison with previous c-means clustering models. Besides being more robust and accurate than previous probabilistic-possibilistic mixture partitions, the product partition is easier to handle due to its reduced number of parameters.

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Authors and Affiliations

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

    László Szilágyi

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  1. László Szilágyi
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Correspondence to László Szilágyi .

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Editors and Affiliations

  1. University of Skövde, School of Informatics University of Skövde, Skövde, Sweden

    Vicenç Torra

  2. University of Skövde, School of Informatics University of Skövde, Skövde, Sweden

    Anders Dahlbom

  3. Toho Gakuen , Tokyo, Japan

    Yasuo Narukawa

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Szilágyi, L. (2017). Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition. In: Torra, V., Dahlbom, A., Narukawa, Y. (eds) Fuzzy Sets, Rough Sets, Multisets and Clustering. Studies in Computational Intelligence, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-319-47557-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-47557-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47556-1

  • Online ISBN: 978-3-319-47557-8

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