hdbscan
Clustering based on density with variable density clusters
Decision gist · record as of 2026-08-14
Yes. HDBSCAN is actively maintained, has no known vulnerabilities, works with current Python versions (3.10–3.14), and solves a real problem—automatic, parameter-light density-based clustering with variable cluster sizes. The medium install friction is manageable given prebuilt wheels. It is well-suited for exploratory clustering and outlier detection.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; compiled C extension requires a compatible build environment or prebuilt wheel for your platform.
- Medium install friction due to compiled C components, but prebuilt wheels are available for Python 3.10, 3.11, 3.12, 3.13, and 3.14 on macOS, Linux, and Windows.
- Maintenance is active with a recent release 74 days ago.
License · maintenance · safety
BSD (permissive) — BSD license is permissive, allowing use in commercial and proprietary projects with minimal restrictions beyond attribution.
last release 2026-06-01 (74 days) · last repo commit 2026-06-12 · 3,137 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,988,759 downloads/mo, #2,798 on PyPI
Alternatives
Verify before relying
pip install hdbscan
import hdbscan
from sklearn.datasets import make_blobs
data, _ = make_blobs(1000)
clusterer = hdbscan.HDBSCAN(min_cluster_size=10)
cluster_labels = clusterer.fit_predict(data)- Whether the package's soft clustering and cluster persistence scoring are suitable for your specific use case.
- Performance characteristics on your dataset size and dimensionality compared to alternatives.
- Compatibility with sparse matrix inputs beyond what the description explicitly confirms.
What it is and what it does
HDBSCAN is a clustering algorithm that extends DBSCAN by performing density-based clustering over varying epsilon values and integrating results to find stable clusters. Unlike DBSCAN, it handles clusters of different densities and is robust to parameter selection—the main tunable parameter, minimum cluster size, is intuitive to set. The package accepts arrays, dataframes, or sparse matrices of shape (num_samples x num_features), or distance matrices between samples, and depends on numpy, scipy, scikit-learn, and joblib.
Beyond basic clustering, HDBSCAN provides outlier detection via the GLOSH algorithm, visualization tools for cluster hierarchies and reachability distances, soft clustering with membership strengths, and cluster persistence scores indicating stability. It also includes a RobustSingleLinkage implementation and a BranchDetector for detecting branching structures in clusters. The implementation prioritizes performance.
Use it for
- Exploratory data analysis on unlabeled datasets where cluster count and density are unknown.
- Outlier detection by accessing outlier_scores_ after fitting to identify anomalous points.
- Clustering with automatic parameter selection when you want to avoid extensive tuning.
- Soft clustering assignments to understand cluster membership confidence for each point.
- Detecting branching or hierarchical structures in cluster data using BranchDetector.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
HDBSCAN is actively maintained, has no known vulnerabilities, works with current Python versions (3.10–3.14), and solves a real problem—automatic, parameter-light density-based clustering with variable cluster sizes. The medium install friction is manageable given prebuilt wheels. It is well-suited for exploratory clustering and outlier detection.
Install
hdbscan on PyPI
Before you install
Medium install friction due to compiled C components, but prebuilt wheels are available for Python 3.10, 3.11, 3.12, 3.13, and 3.14 on macOS, Linux, and Windows. Maintenance is active with a recent release 74 days ago.
Requires Python 3.10 or later; compiled C extension requires a compatible build environment or prebuilt wheel for your platform.
License in practice
BSD license is permissive, allowing use in commercial and proprietary projects with minimal restrictions beyond attribution.
Quickstart
pip install hdbscan
import hdbscan
from sklearn.datasets import make_blobs
data, _ = make_blobs(1000)
clusterer = hdbscan.HDBSCAN(min_cluster_size=10)
cluster_labels = clusterer.fit_predict(data)Verify before relying
- Whether the package's soft clustering and cluster persistence scoring are suitable for your specific use case.
- Performance characteristics on your dataset size and dimensionality compared to alternatives.
- Compatibility with sparse matrix inputs beyond what the description explicitly confirms.
Package facts
| License | BSD permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 4 packagesnumpyscipyscikit-learnjoblib |
| Maintenance | Actively maintained 74 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,988,759 / month, #2,798 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI ApprovedOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: CProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: hdbscan-0.8.44-cp310-cp310-macosx_10_9_universal2.whl; hdbscan-0.8.44-cp310-cp310-macosx_15_0_x86_64.whl; hdbscan-0.8.44-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; hdbscan-0.8.44-cp310-cp310-win_amd64.whl; hdbscan-0.8.44-cp311-cp311-macosx_10_9_universal2.whl; hdbscan-0.8.44-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; hdbscan-0.8.44-cp311-cp311-win_amd64.whl; hdbscan-0.8.44-cp312-cp312-macosx_10_13_universal2.whl; hdbscan-0.8.44-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; hdbscan-0.8.44-cp312-cp312-win_amd64.whl; hdbscan-0.8.44-cp313-cp313-macosx_10_13_universal2.whl; hdbscan-0.8.44-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; hdbscan-0.8.44-cp313-cp313-win_amd64.whl; hdbscan-0.8.44-cp314-cp314-macosx_10_15_universal2.whl; hdbscan-0.8.44-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; hdbscan-0.8.44-cp314-cp314-win_amd64.whl
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