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 ). 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'.