Explainable Graph Spectral Clustering For Text Embeddings
Mieczysław A. Kłopotek, Sławomir T. Wierzchoń, Bartłomiej Starosta, Piotr Borkowski, Dariusz Czerski, Eryk Laskowski
arXiv:2508.14075v1h-index: 9
Originality Synthesis-oriented
AI Analysis
This work addresses interpretability in text clustering for researchers and practitioners, but it is incremental as it builds on prior work.
The paper tackles the problem of making graph spectral clustering results explainable for text embeddings, generalizing a previous approach from term vector space to other embeddings like GloVe.
In a previous paper, we proposed an introduction to the explainability of Graph Spectral Clustering results for textual documents, given that document similarity is computed as cosine similarity in term vector space. In this paper, we generalize this idea by considering other embeddings of documents, in particular, based on the GloVe embedding idea.