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A Survey on Kernelized Fuzzy Clustering Tools for Data Processing

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Advances in Computer Science for Engineering and Education IV (ICCSEEA 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 83))

Abstract

Although most of the well-known fuzzy clustering algorithms are somewhat sensitive to noise, there are some more profound possibilistic fuzzy clustering techniques based on kernel distance metrics that are not subject to this problem. The paper presents a survey on this type of fuzzy clustering methods. Meanwhile, introducing a particular distance measure based on the Bregman divergence to a fuzzy clustering tool made it possible to improve the algorithm’s performance compared to traditional Euclidean-based analogs. A bunch of experimental evaluation is performed for this set of methods.

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Authors and Affiliations

  1. National Aviation University, Liubomyra Huzara Ave 1, Kyiv, 03058, Ukraine

    Zhengbing Hu

  2. Institute for Research and Applications of Fuzzy Modeling, CE IT4Innovations, University of Ostrava, 30. dubna 22, 701 03, Ostrava, Czech Republic

    Oleksii K. Tyshchenko

Authors
  1. Zhengbing Hu
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  2. Oleksii K. Tyshchenko
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Editor information

Editors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, China

    Zhengbing Hu

  2. Mechanical Engineering Research Institute of the Russian Academy of Sciences, Moscow, Russia

    Sergey Petoukhov

  3. Faculty of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”, Kiev, Ukraine

    Ivan Dychka

  4. Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Plantation, FL, USA

    Matthew He

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Hu, Z., Tyshchenko, O.K. (2021). A Survey on Kernelized Fuzzy Clustering Tools for Data Processing. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education IV. ICCSEEA 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-80472-5_24

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