scikit-learn-contrib/hdbscan
A high performance implementation of HDBSCAN clustering.
Enables variable-density clustering by performing DBSCAN across epsilon scales and selecting the most stable result, eliminating manual parameter tuning. Provides soft clustering via membership strengths, outlier detection through the GLOSH algorithm, and visualization tools for cluster hierarchies and reachability trees. Follows scikit-learn's API conventions while supporting arrays, sparse matrices, and distance matrices as input.
3,080 stars and 1,686,801 monthly downloads. Used by 16 other packages. Actively maintained with 5 commits in the last 30 days. Available on PyPI.
Stars
3,080
Forks
531
Language
Jupyter Notebook
License
BSD-3-Clause
Category
Last pushed
Jan 26, 2026
Monthly downloads
1,686,801
Commits (30d)
5
Dependencies
4
Reverse dependents
16
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