Interpretable Clustering Using Dempster-Shafer Theory

This study presents DSClustering, a novel algorithm that merges clustering validity with interpretability using the Dempster-Shafer theory. Traditional clustering methods like K-means, DBSCAN, and agglomerative clustering, while widely used for their efficiency and accuracy, often fall short in tran...

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Bibliographic Details
Main Authors: Aram Adamyan, Hovhannes Hovanesyan, Daniel Radrigan, Nelson Baloian, Ashot Harutyunyan
Format: Article
Language:English
Published: Graz University of Technology 2025-08-01
Series:Journal of Universal Computer Science
Subjects:
Online Access:https://lib.jucs.org/article/164694/download/pdf/
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