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deadwood: Outlier Detection via Pruning Mutual Reachability Minimum
Spanning Trees
Implements an anomaly detection algorithm based on a dataset's
mutual reachability minimum spanning tree: 'deadwood' prunes
protruding tree segments and marks small debris as outliers;
see Gagolewski (2026) <https://deadwood.gagolewski.com/>.
More precisely, tree edges with weights greater than the detected elbow
point are removed. All the resulting connected components whose sizes do
not exceed a prespecified threshold are deemed anomalous. The use of
a mutual reachability distance pulls peripheral observations farther away
from one another. If the dataset is comprised of well-separated clusters
of heterogeneous densities, an attempt to split the dataset and refine
the outlierness markers will be made.
The 'Python' version of 'deadwood' is available via 'PyPI'.
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