Rough Sets for Explainability of Spectral Graph Clustering
This work addresses explainability for users of spectral clustering in domains like text analysis, but it is incremental as it builds on prior research.
The paper tackles the problem of explaining spectral graph clustering results, especially for text documents, by enhancing an existing explanation methodology using rough set theory to address issues with unclear content and algorithm stochasticity.
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.