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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.12450 |
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| _version_ | 1866909739888148480 |
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| author | Pepin, Étienne |
| author_facet | Pepin, Étienne |
| contents | This article presents an adaptive mean shift algorithm designed for datasets with varying local scale and cluster cardinality. Local distance distributions, from a point to all others, are used to estimate the cardinality of the local cluster by identifying a local minimum in the density of the distance distribution. Based on these cardinality estimates, local cluster parameters are then computed for the entire cluster in contrast to KDE-based methods, which provide insight only into localized regions of the cluster. During the mean shift execution, the cluster cardinality estimate is used to adaptively adjust the bandwidth and the mean shift kernel radius threshold. Our algorithm outperformed a recently proposed adaptive mean shift method on its original dataset and demonstrated competitive performance on a broader clustering benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Local Cluster Cardinality Estimation for Adaptive Mean Shift Pepin, Étienne Machine Learning I.5.3 This article presents an adaptive mean shift algorithm designed for datasets with varying local scale and cluster cardinality. Local distance distributions, from a point to all others, are used to estimate the cardinality of the local cluster by identifying a local minimum in the density of the distance distribution. Based on these cardinality estimates, local cluster parameters are then computed for the entire cluster in contrast to KDE-based methods, which provide insight only into localized regions of the cluster. During the mean shift execution, the cluster cardinality estimate is used to adaptively adjust the bandwidth and the mean shift kernel radius threshold. Our algorithm outperformed a recently proposed adaptive mean shift method on its original dataset and demonstrated competitive performance on a broader clustering benchmark. |
| title | Local Cluster Cardinality Estimation for Adaptive Mean Shift |
| topic | Machine Learning I.5.3 |
| url | https://arxiv.org/abs/2508.12450 |