[2512.12436] Rough Sets for Explainability of Spectral Graph Clustering

Computer Science > Machine Learning arXiv:2512.12436 (cs) [Submitted on 13 Dec 2025 (v1), last revised 27 Feb 2026 (this version, v2)] Title:Rough Sets for Explainability of Spectral Graph Clustering Authors:Bartłomiej Starosta, Sławomir T. Wierzchoń, Piotr Borkowski, Dariusz Czerski, Marcin Sydow, Eryk Laskowski, Mieczysław A. Kłopotek View a PDF of the paper titled Rough Sets for Explainability of Spectral Graph Clustering, by Bart{\l}omiej Starosta and S{\l}awomir T. Wierzcho\'n and Piotr Borkowski and Dariusz Czerski and Marcin Sydow and Eryk Laskowski and Mieczys{\l}aw A. K{\l}opotek View PDF HTML (experimental) Abstract:Graph Spectral Clustering methods (GSC) allow representing clusters of diverse shapes, densities, etc. However, the results of such algorithms, when applied e.g. to text documents, are hard to explain to the user, especially due to embedding in the spectral space which has no obvious relation to document contents. Furthermore, the presence of documents without clear content meaning and the stochastic nature of the clustering algorithms deteriorate explainability. This paper proposes an enhancement to the explanation methodology, proposed in an earlier research of our team. It allows us to overcome the latter problems by taking inspiration from rough set theory. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2512.12436 [cs.LG]   (or arXiv:2512.12436v2 [cs.LG] for this version)   https://doi.org/10.48550/arX...