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Clustering-Based Adaptive Self-Organizing Map

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Artificial Intelligence and Soft Computing (ICAISC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12854))

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Abstract

We propose an improvement of the Self-Organizing Map (SOM). In our version of SOM, the neighborhood widths of the Best Matching Units (BMUs) are computed on the basis of the data density and scattering in the input data space. The density and scattering are expressed by the values of the inner-cluster variances, which are obtained after the preliminary input data clustering. The experiments conducted on the two real datasets evaluated the proposed approach on the basis of a comparison with the three reference data visualization methods. By reporting the superiority of our technique over the other tested algorithms, we confirmed the effectiveness and accuracy of the introduced solution.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland

    Dominik Olszewski

Authors
  1. Dominik Olszewski
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Correspondence to Dominik Olszewski .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Olszewski, D. (2021). Clustering-Based Adaptive Self-Organizing Map. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12854. Springer, Cham. https://doi.org/10.1007/978-3-030-87986-0_16

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  • DOI: https://doi.org/10.1007/978-3-030-87986-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87985-3

  • Online ISBN: 978-3-030-87986-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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