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Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees
arXiv stat.ML0 views
arXiv:2604.07143v2 Announce Type: replace-cross 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 a divisive version of Genie and an alternative to HDBSCAN that produces partitions with user-specified sizes. A fast, easy-to-use implementation of this 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.
Source：https://arxiv.org/abs/2604.07143PreviousMinimaxity and Admissibility of Bayesian Neural NetworksNextLinear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction
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