4 papers
Rough Sets for Explainability of Spectral Graph Clustering
Bartłomiej Starosta, Sławomir T. Wierzchoń, Piotr Borkowski +4
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,…
Explainable Graph Spectral Clustering For GloVe-like Text Embeddings
Mieczysław A. Kłopotek, Sławomir T. Wierzchoń, Bartłomiej Starosta +3
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 cos…
A Method for Handling Negative Similarities in Explainable Graph Spectral Clustering of Text Documents -- Extended Version
Mieczysław A. Kłopotek, Sławomir T. Wierzchoń, Bartłomiej Starosta +2
This paper investigates the problem of Graph Spectral Clustering with negative similarities, resulting from document embeddings different from the traditional Term Vector Space (li…
Semantic classifier approach to document classification
Piotr Borkowski, Krzysztof Ciesielski, Mieczysław A. Kłopotek
In this paper we propose a new document classification method, bridging discrepancies (so-called semantic gap) between the training set and the application sets of textual data. We…