Fuzzy Clustering with Uninorm-Based Distance Measure

Evgeny Kagan (), Alexander Novoselsky and Alexander Rybalov
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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

Mathematics, 2025, vol. 13, issue 10, 1-20

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.

Keywords: fuzzy c -means clustering; uninorm; absorbing norm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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