A HYBRID MODEL FOR TEXT CLUSTERING BASED ON FS-DBSCAN
Received: 2026-07-15 10:34:11
Published: 2026-04-18
Abstract
This paper considers the problem of clustering textual data in a high-dimensional feature space. A hybrid clustering model based on the FS-DBSCAN (Feature Stability DBSCAN) algorithm is proposed. The method is focused on analyzing the structure of data with different density distributions and allows interpreting clustering results through the stability of object status configurations. To evaluate the similarity of textual documents, the cosine similarity measure is used. Computational experiments were conducted on the XimTex dataset containing scientific text documents. The obtained results demonstrate that the proposed method effectively detects clusters and identifies anomalous objects in text data.
Keywords
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