lumbermark: Resistant Clustering via Chopping Up Mutual Reachability Minimum
Spanning Trees
Implements a fast and resistant divisive clustering algorithm which
identifies a specified number of clusters: 'lumbermark' iteratively
chops off sizeable limbs that are joined by protruding segments
of a dataset's mutual reachability minimum spanning tree
(Gagolewski, 2026 <doi:10.48550/arXiv.2604.07143>). The use of a mutual
reachability distance pulls peripheral points farther away from each other.
It is a viable alternative to the 'HDBSCAN*' algorithm
and can be viewed as a divisive version of Genie.
The resulting partitions of different granularities are properly nested.
When combined with the 'deadwood' package, it can act as an outlier detector.
The 'Python' version of 'lumbermark' is available via 'PyPI'.
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