# Rough Sets for Explainability of Spectral Graph Clustering

arXiv: https://arxiv.org/abs/2512.12436 · HTML: https://mlsanity.com/paper/2512.12436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2025.

## Built on by (most cited first, 1 of 1)

- [The K-SCAN Clustering Algorithm](https://mlsanity.com/paper/2607.24537/) (2026)

## Abstract (arXiv)

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.