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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| Format: | Article |
| Language: | English |
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Graz University of Technology
2025-08-01
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| Series: | Journal of Universal Computer Science |
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| Online Access: | https://lib.jucs.org/article/164694/download/pdf/ |
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| _version_ | 1849235660695666688 |
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| author | Aram Adamyan Hovhannes Hovanesyan Daniel Radrigan Nelson Baloian Ashot Harutyunyan |
| author_facet | Aram Adamyan Hovhannes Hovanesyan Daniel Radrigan Nelson Baloian Ashot Harutyunyan |
| author_sort | Aram Adamyan |
| collection | DOAJ |
| description | 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 transparency, creating barriers in critical fields such as healthcare, finance, and consumer analytics where decision-making requires clear, interpretable insights. DSClustering aims to bridge this gap by assigning clusters based on belief functions from Dempster-Shafer theory, which allows it to generate rule-based explanations for each data point’s cluster assignment. Through detailed experiments on real-world datasets, including consumer behavior and airline satisfaction data, we evaluate DSClustering against traditional algorithms using key metrics such as Silhouette score, Rand index and Dunn’s index for clustering validity. The results indicate that DSClustering not only performs competitively but also offers a clear interpretative layer, making it suitable for applications where understanding model outputs is as essential as the accuracy of the outputs themselves. This work underscores the increasing importance of interpretability in machine learning, particularly in unsupervised learning, where transparency is typically challenging to achieve. DSClustering demonstrates a promising approach for balancing robust clustering with user-oriented interpretability, potentially encouraging broader adoption of interpretable clustering models in data-critical industries. |
| format | Article |
| id | doaj-art-ed830c3235be4f81bce6d53d1941d851 |
| institution | Kabale University |
| issn | 0948-6968 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj-art-ed830c3235be4f81bce6d53d1941d8512025-08-20T04:02:44ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682025-08-01319980100310.3897/jucs.164694164694Interpretable Clustering Using Dempster-Shafer TheoryAram Adamyan0Hovhannes Hovanesyan1Daniel Radrigan2Nelson Baloian3Ashot Harutyunyan4American University of ArmeniaAmerican University of ArmeniaUniversidad de ChileUniversidad de ChileYerevan State UniversityThis 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 transparency, creating barriers in critical fields such as healthcare, finance, and consumer analytics where decision-making requires clear, interpretable insights. DSClustering aims to bridge this gap by assigning clusters based on belief functions from Dempster-Shafer theory, which allows it to generate rule-based explanations for each data point’s cluster assignment. Through detailed experiments on real-world datasets, including consumer behavior and airline satisfaction data, we evaluate DSClustering against traditional algorithms using key metrics such as Silhouette score, Rand index and Dunn’s index for clustering validity. The results indicate that DSClustering not only performs competitively but also offers a clear interpretative layer, making it suitable for applications where understanding model outputs is as essential as the accuracy of the outputs themselves. This work underscores the increasing importance of interpretability in machine learning, particularly in unsupervised learning, where transparency is typically challenging to achieve. DSClustering demonstrates a promising approach for balancing robust clustering with user-oriented interpretability, potentially encouraging broader adoption of interpretable clustering models in data-critical industries.https://lib.jucs.org/article/164694/download/pdf/ClusteringInterpretabilityDempster-ShaferMac |
| spellingShingle | Aram Adamyan Hovhannes Hovanesyan Daniel Radrigan Nelson Baloian Ashot Harutyunyan Interpretable Clustering Using Dempster-Shafer Theory Journal of Universal Computer Science Clustering Interpretability Dempster-Shafer Mac |
| title | Interpretable Clustering Using Dempster-Shafer Theory |
| title_full | Interpretable Clustering Using Dempster-Shafer Theory |
| title_fullStr | Interpretable Clustering Using Dempster-Shafer Theory |
| title_full_unstemmed | Interpretable Clustering Using Dempster-Shafer Theory |
| title_short | Interpretable Clustering Using Dempster-Shafer Theory |
| title_sort | interpretable clustering using dempster shafer theory |
| topic | Clustering Interpretability Dempster-Shafer Mac |
| url | https://lib.jucs.org/article/164694/download/pdf/ |
| work_keys_str_mv | AT aramadamyan interpretableclusteringusingdempstershafertheory AT hovhanneshovanesyan interpretableclusteringusingdempstershafertheory AT danielradrigan interpretableclusteringusingdempstershafertheory AT nelsonbaloian interpretableclusteringusingdempstershafertheory AT ashotharutyunyan interpretableclusteringusingdempstershafertheory |