erdogant/clusteval
Clusteval provides methods for unsupervised cluster validation
When you're trying to group your data into meaningful categories, this tool helps you figure out the best way to do it. You feed it your raw data, and it tells you how many groups are ideal and how well those groups are formed, using various evaluation metrics like silhouette and Davies-Bouldin index. Data scientists, researchers, and analysts who need to confidently interpret data clusters will find this useful.
Available on PyPI.
Use this if you need to determine the optimal number of clusters and validate the quality of your clustering results in an unsupervised learning context.
Not ideal if you already know the number of clusters you want or if you are looking for a supervised classification tool.
Stars
70
Forks
8
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 21, 2026
Commits (30d)
0
Dependencies
11
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/erdogant/clusteval"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
scikit-learn-contrib/hdbscan
A high performance implementation of HDBSCAN clustering.
annoviko/pyclustering
pyclustering is a Python, C++ data mining library.
panagiotisanagnostou/HiPart
Hierarchical divisive clustering algorithm execution, visualization and Interactive visualization.
mqcomplab/MDANCE
MDANCE: O(N) clustering for molecular dynamics. Process 1.5M frames in 40min. 8 specialized algorithms.
wq2012/SpectralCluster
Python re-implementation of the (constrained) spectral clustering algorithms used in Google's...