hdbscan

cluster-analysis clustering clustering-algorithm clustering-evaluation machine-learning machine-learning-algorithms

Score: 33.370758410056226

Last synced: about 16 hours ago
JSON representation

Repository metadata:

A high performance implementation of HDBSCAN clustering.

Host: GitHub
URL: https://github.com/scikit-learn-contrib/hdbscan
Owner: scikit-learn-contrib
License: bsd-3-clause
Created: 2015-04-22T13:32:37.000Z (almost 11 years ago)
Default Branch: master
Last Pushed: 2025-12-11T21:04:02.000Z (about 2 months ago)
Last Synced: 2025-12-20T15:52:25.781Z (about 2 months ago)
Topics: cluster-analysis, clustering, clustering-algorithm, clustering-evaluation, machine-learning, machine-learning-algorithms
Language: Jupyter Notebook
Homepage: http://hdbscan.readthedocs.io/en/latest/
Size: 27.8 MB
Stars: 3,044
Watchers: 54
Forks: 525
Open Issues: 381
Metadata Files:
- Readme: README.rst
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Owner metadata:

Name: scikit-learn-contrib
Login: scikit-learn-contrib
Email:
Kind: organization
Description: scikit-learn compatible projects
Website: http://contrib.scikit-learn.org
Location:
Twitter:
Company:
Icon url: https://avatars.githubusercontent.com/u/17349883?v=4
Repositories: 27
Last Synced at: 2024-03-25T20:03:09.812Z
Profile URL: https://github.com/scikit-learn-contrib

GitHub Events

Total

Create event: 1
Fork event: 22
Issue comment event: 45
Issues event: 17
Member event: 1
Pull request event: 24
Pull request review comment event: 3
Pull request review event: 6
Push event: 7
Release event: 2
Watch event: 187
Total: 315

Last Year

Fork event: 13
Issue comment event: 29
Issues event: 13
Member event: 1
Pull request event: 16
Pull request review event: 3
Push event: 5
Release event: 1
Watch event: 149
Total: 230

Committers metadata

Last synced: 26 days ago

Total Commits: 894
Total Committers: 97
Avg Commits per committer: 9.216
Development Distribution Score (DDS): 0.29

Commits in past year: 12
Committers in past year: 5
Avg Commits per committer in past year: 2.4
Development Distribution Score (DDS) in past year: 0.583

Name	Email	Commits
Leland McInnes	l**s@g**m	635
Jelmer Bot	j**t@g**m	22
neontty	n**y@g**m	11
Guillaume Lemaitre	g**e@v**u	10
John Healy	j**l@g**m	10
Steve Astels	s**s@g**m	10
Greg Demand	6****d	9
luis261	l**r@g**e	8
gclendenning	6****g	8
jc-healy	j**y@g**m	8
Guillaume Ansanay-Alex	g**y@g**m	7
Dicksonchin93	c**n@d**m	7
João Matias	j**s@t**m	7
gclen	g**g@u**t	7
Bruno Alano	b**o@n**r	7
Matthew Carrigan	r**1@g**m	6
Sebastian Berg	s**b@n**m	5
cmalzer	c**r@g**e	5
Rhaedonius	l**o@a**n	5
Lukas Großberger	c**e@g**z	4
the null	4****e	4
Ryan Helinski	r**i@g**m	4
m-dz	m**c@g**m	4
Nathaniel Saul	n**t@s**m	4
Adam Lugowski	a**i@g**m	3
areeh	a**t@i**m	3
cmalzer	c**r@g**e	3
gr	g**y@g**m	3
David Thomson	d**n@l**n	2
Jinhua Wang	j**g@u**k	2
and 67 more...

Issue and Pull Request metadata

Last synced: about 2 months ago

Total issues: 159
Total pull requests: 70
Average time to close issues: 6 months
Average time to close pull requests: 3 months
Total issue authors: 152
Total pull request authors: 36
Average comments per issue: 4.03
Average comments per pull request: 1.41
Merged pull request: 44
Bot issues: 0
Bot pull requests: 0

Past year issues: 16
Past year pull requests: 19
Past year average time to close issues: 13 days
Past year average time to close pull requests: 27 days
Past year issue authors: 15
Past year pull request authors: 7
Past year average comments per issue: 0.63
Past year average comments per pull request: 0.58
Past year merged pull request: 11
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/scikit-learn-contrib/hdbscan

Top Issue Authors

notluquis (3)
lucetka (2)
katzurik (2)
divya-agrawal3103 (2)
KukumavMozolo (2)
jakirkham (2)
aleereza (1)
mdagost (1)
xavgit (1)
OlgaGKononova (1)
SergioG-M (1)
PezAmaury (1)
jgonggrijp (1)
mczerny (1)
huijiawu0 (1)

Top Pull Request Authors

JelmerBot (13)
lmcinnes (5)
gclendenning (5)
chenxinye (4)
NicoSantamaria (2)
smartIU (2)
prodrigues-tdx (2)
Rhaedonius (2)
Antobiotics (2)
divyegala (2)
axiak (2)
seberg (2)
meshari343 (2)
joaquindas (2)
cearlefraym (2)

Top Issue Labels

help wanted (1)
new feature (1)
bug (1)

Top Pull Request Labels

Package metadata

Total packages: 3
Total downloads:
- pypi: 1,359,660 last-month
Total docker downloads: 934,700,138
Total dependent packages: 133 (may contain duplicates)
Total dependent repositories: 522 (may contain duplicates)
Total versions: 88
Total maintainers: 4

pypi.org: hdbscan

Clustering based on density with variable density clusters

Homepage: http://github.com/scikit-learn-contrib/hdbscan
Documentation: https://hdbscan.readthedocs.io/
Licenses: BSD
Latest release: 0.8.41 (published about 2 months ago)
Last Synced: 2026-01-16T13:58:31.074Z (21 days ago)
Versions: 58
Dependent Packages: 120
Dependent Repositories: 483
Downloads: 1,359,660 Last month
Docker Downloads: 934,700,138
Rankings:
- Dependent packages count: 0.173%
- Docker downloads count: 0.295%
- Downloads: 0.477%
- Dependent repos count: 0.648%
- Average: 0.918%
- Stargazers count: 1.451%
- Forks count: 2.463%
Maintainers (3)

conda-forge.org: hdbscan

Homepage: http://github.com/scikit-learn-contrib/hdbscan
Licenses: BSD-3-Clause
Latest release: 0.8.29 (published over 3 years ago)
Last Synced: 2025-12-23T07:31:09.388Z (about 2 months ago)
Versions: 28
Dependent Packages: 11
Dependent Repositories: 39
Rankings:
- Dependent packages count: 5.484%
- Dependent repos count: 5.809%
- Average: 6.743%
- Forks count: 7.62%
- Stargazers count: 8.059%

spack.io: py-hdbscan

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. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any).

Homepage: https://github.com/scikit-learn-contrib/hdbscan
Licenses: []
Latest release: 0.8.29 (published over 2 years ago)
Last Synced: 2025-12-23T07:30:55.703Z (about 2 months ago)
Versions: 2
Dependent Packages: 2
Dependent Repositories: 0
Rankings:
- Dependent repos count: 0.0%
- Forks count: 4.321%
- Stargazers count: 4.599%
- Average: 9.247%
- Dependent packages count: 28.067%
Maintainers (1)
- adamjstewart

Dependencies

.github/workflows/pythonpublish.yml actions

actions/checkout v1 composite
actions/setup-python v1 composite

.github/workflows/pythonpublish_windows.yml actions

actions/checkout v1 composite
actions/setup-python v1 composite

requirements.txt pypi

cython >=0.27
joblib >=1.0
numpy >=1.20
scikit-learn >=0.20
scipy >=1.0

.github/workflows/pythonpublish_wheel.yml actions

actions/checkout v1 composite
actions/setup-python v1 composite

pyproject.toml pypi

setup.py pypi

environment.yml conda

hdbscan >=0.8.11
matplotlib >=2.0
python >=3.5
scikit-learn >=0.19
seaborn >=0.8

docs/docs_requirements.txt pypi

sphinx_rtd_theme *

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