Rough Sets for Explainability of Spectral Graph Clustering

2025/12/13 by Bartłomiej Starosta, Sławomir T. Wierzchoń, Starosta, Bartłomiej +11 · 1 citation
Computer Science · #Advanced Clustering Algorithms Research #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Graph Theory and Algorithms #Machine Learning (cs.LG) #Rough Sets and Fuzzy Logic

paper · pdf · doi:10.48550/arxiv.2512.12436

openalex publication_date 2025/12/13 · openalex created_date 2025/12/17 · openalex updated_date 2026/07/28

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.

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