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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.06587 |
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| _version_ | 1866913685193097216 |
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| author | Soubeiga, Armel Antoine, Violaine |
| author_facet | Soubeiga, Armel Antoine, Violaine |
| contents | A recent developing trend in clustering is the advancement of algorithms that not only identify clusters within data, but also express and capture the uncertainty of cluster membership. Evidential clustering addresses this by using the Dempster-Shafer theory of belief functions, a framework designed to manage and represent uncertainty. This approach results in a credal partition, a structured set of mass functions that quantify the uncertain assignment of each object to potential groups. The Python framework evclust, presented in this paper, offers a suite of efficient evidence clustering algorithms as well as tools for visualizing, evaluating and analyzing credal partitions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06587 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | evclust: Python library for evidential clustering Soubeiga, Armel Antoine, Violaine Software Engineering Computer Vision and Pattern Recognition Machine Learning A recent developing trend in clustering is the advancement of algorithms that not only identify clusters within data, but also express and capture the uncertainty of cluster membership. Evidential clustering addresses this by using the Dempster-Shafer theory of belief functions, a framework designed to manage and represent uncertainty. This approach results in a credal partition, a structured set of mass functions that quantify the uncertain assignment of each object to potential groups. The Python framework evclust, presented in this paper, offers a suite of efficient evidence clustering algorithms as well as tools for visualizing, evaluating and analyzing credal partitions. |
| title | evclust: Python library for evidential clustering |
| topic | Software Engineering Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2502.06587 |