scikit-learn-contrib / hdbscan
A high performance implementation of HDBSCAN clustering.
Why this job: works in the areas we cover, but does not say which job it does
What it does
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 6 days ago
- Stars
- 3,160
- +2 this week
- Licence
- BSD-3-Clause
- Allows business use
- Downloads for
- None listed
- No release files
- Latest tag
- release-0.8.44
- Released 5 months ago
- Built with
- Jupyter Notebook
- 94% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 33% closed
- 356 still open
- Pull requests
- 166 merged
- 46 waiting
- Releases
- 60
- 4 in the last year
- No contributing guide
- No security policy
- Code of conduct (opens in new tab)
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More: languages, topics and project record
Languages
- Jupyter Notebook 94%
- Python 4%
- Cython 2%
- TeX <1%
Topics
- machine-learning
- machine-learning-algorithms
- clustering
- clustering-algorithm
- cluster-analysis
- clustering-evaluation
Project record
- JobOther AI tools
- StatusListed
- Commits1,165
- Forks550
- Watching52
- Stars in 28 days+6
- CreatedApr 22, 2015
- First seen by usNov 7, 2026
- CheckedNov 8, 2026