Rough Sets for Explainability of Spectral Graph Clustering

2025-12-13

AI Summary

Graph spectral clustering (GSC) applied to text suffers from poor interpretability due to semantic disconnection between the embedded spectral space and original semantics, interference from noisy documents, and algorithmic randomness. To address this, we propose the first unsupervised, content-aware interpretability enhancement framework for GSC grounded in rough set theory. Our method first obtains cluster structure via graph Laplacian eigendecomposition and k-means spectral clustering; it then constructs upper and lower approximations under term-frequency–guided semantic constraints to quantify cluster semantic stability and structural uncertainty. Evaluated on multiple text datasets, our approach improves explanation fidelity by 32% and user comprehension consistency by 27%, without compromising clustering quality. The core innovation lies in the deep integration of rough set boundary region analysis with spectral clustering—enabling semantically traceable and quantifiably stable interpretable clustering.

Technology Category

• Machine Learning: Clustering
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Explains spectral clustering results for text documents
• Addresses unclear document content and algorithm randomness
• Enhances explainability using rough set theory

Innovation

• Rough set theory enhances spectral clustering explainability
• Addresses unclear document content and stochastic algorithm issues
• Improves user understanding of spectral space embeddings