Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866913016162811904 |
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| author | Gagolewski, Marek |
| author_facet | Gagolewski, Marek |
| contents | We introduce Lumbermark, a robust divisive clustering algorithm capable of detecting clusters of varying sizes, densities, and shapes. Lumbermark iteratively chops off large limbs connected by protruding segments of a dataset's mutual reachability minimum spanning tree. The use of mutual reachability distances smoothens the data distribution and decreases the influence of low-density objects, such as noise points between clusters or outliers at their peripheries. The algorithm can be viewed as an alternative to HDBSCAN that produces partitions with user-specified sizes. A fast, easy-to-use implementation of the new method is available in the open-source 'lumbermark' package for Python and R. We show that Lumbermark performs well on benchmark data and hope it will prove useful to data scientists and practitioners across different fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07143 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees Gagolewski, Marek Machine Learning Applications We introduce Lumbermark, a robust divisive clustering algorithm capable of detecting clusters of varying sizes, densities, and shapes. Lumbermark iteratively chops off large limbs connected by protruding segments of a dataset's mutual reachability minimum spanning tree. The use of mutual reachability distances smoothens the data distribution and decreases the influence of low-density objects, such as noise points between clusters or outliers at their peripheries. The algorithm can be viewed as an alternative to HDBSCAN that produces partitions with user-specified sizes. A fast, easy-to-use implementation of the new method is available in the open-source 'lumbermark' package for Python and R. We show that Lumbermark performs well on benchmark data and hope it will prove useful to data scientists and practitioners across different fields. |
| title | Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2604.07143 |