clusteval is a python package that is developed to evaluate detected clusters and return the cluster labels that have most optimal clustering tendency, Number of clusters and clustering quality. Multiple evaluation strategies are implemented for the evaluation; silhouette, dbindex, and derivative, and four clustering methods can be used: agglomerative, kmeans, dbscan and hdbscan.
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- A step-by-step guide for clustering images
- Detection of Duplicate Images Using Image Hash Functions
- From Data to Clusters: When is Your Clustering Good Enough?
- From Clusters To Insights; The Next Step
Full documentation is available at erdogant.github.io/clusteval, including examples and API references.
It is advisable to use a virtual environment:
conda create -n env_clusteval python=3.12
conda activate env_clustevalInstall via PyPI:
pip install clustevalTo upgrade to the latest version:
pip install --upgrade clustevalImport the library:
from clusteval import clustevalA structured overview is available in the documentation.
Silhouette Score |
Optimal Clusters |
Dendrogram |
Davies-Bouldin Index |
Derivative Method |
DBSCAN |
HDBSCAN A |
HDBSCAN B |
Please cite clusteval in your publications if it has been helpful in your research. Citation information is available at the top right of the GitHub page.
- Use ARI when clustering contains large equal-sized clusters
- Use AMI for unbalanced clusters with small components
- Adjusted Rand Score — scikit-learn
- Adjusted for Chance Measures — scikit-learn
- imagededup GitHub repo
- Clustering images by visual similarity
- Facebook DeepCluster
- PCA on Hyperspectral Data
- Face Recognition with PCA
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