#13 · Primary category： Classical Machine Learning Frameworks
hdbscan
A high performance implementation of HDBSCAN clustering.
Project last updated：06/12/26
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3.1K
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534
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105
License
BSD-3-Clause
Why we included this project
HDBSCAN is a density-based clustering method that finds groups of varying density, and unlike DBSCAN it needs almost no tuning: you set a minimum cluster size and it returns a stable clustering. The package plugs into scikit-learn pipelines with the usual fit/predict API, accepts arrays, dataframes, sparse matrices, or precomputed distance matrices, and is written in Cython so it stays fast on large datasets. Beyond plain clustering it adds soft cluster membership strengths, persistence scores, GLOSH outlier detection, and robust single linkage, plus plotting tools for inspecting the cluster hierarchy. That makes it a solid choice for exploratory data analysis, customer segmentation, and anomaly screening.
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