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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;
see Gagolewski (2026) <https://lumbermark.gagolewski.com/>.
The use of a mutual reachability distance pulls peripheral points farther
away from each other. 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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