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Fuzzy Clustering with Uninorm-Based Distance Measure

Author

Listed:
  • Evgeny Kagan

    (Department Industrial Engineering, Ariel University, Kiryat ha-Mada, Ariel 4070000, Israel)

  • Alexander Novoselsky

    (Independent Researcher, Tel Aviv 6158101, Israel)

  • Alexander Rybalov

    (LAMBDA Lab, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel)

Abstract

In this paper, we suggest an algorithm of fuzzy clustering with a uninorm-based distance measure. The algorithm follows a general scheme of fuzzy c -means (FCM) clustering, but in contrast to the existing algorithm, it implements logical distance between data instances. The centers of the clusters calculated by the algorithm are less dispersed and are concentrated in the areas of the actual centers of the clusters that result in the more accurate recognition of the number of clusters and of data structure.

Suggested Citation

  • Evgeny Kagan & Alexander Novoselsky & Alexander Rybalov, 2025. "Fuzzy Clustering with Uninorm-Based Distance Measure," Mathematics, MDPI, vol. 13(10), pages 1-20, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:10:p:1661-:d:1659274
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    References listed on IDEAS

    as
    1. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
    2. Alaa Ghanaiem & Evgeny Kagan & Parteek Kumar & Tal Raviv & Peter Glynn & Irad Ben-Gal, 2023. "Unsupervised Classification under Uncertainty: The Distance-Based Algorithm," Mathematics, MDPI, vol. 11(23), pages 1-19, November.
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