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arXiv · 2604.07143

Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees

Marek Gagolewski
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Abstract

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.

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Marek Gagolewski. 2026-04-08. Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees. https://arxiv.org/abs/2604.07143

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