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

arXiv: https://arxiv.org/abs/2604.07143 · HTML: https://mlsanity.com/paper/2604.07143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [A framework for benchmarking clustering algorithms](https://mlsanity.com/paper/2209.09493/) (2022)
- [Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm](https://mlsanity.com/paper/2209.05757/) (2022)
- [Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering](https://mlsanity.com/paper/2104.01126/) (2021)
- [Clustering with minimum spanning trees: How good can it be?](https://mlsanity.com/paper/2303.05679/) (2023)
- [A single-tree algorithm to compute the Euclidean minimum spanning tree on GPUs](https://mlsanity.com/paper/2207.00514/) (2022)

## Abstract (arXiv)

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.