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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On the documentation pages you can find detailed information about the working of the clusteval with many examples.
conda create -n env_clusteval python=3.8
conda activate clustevalpip install clustevalfrom clusteval import clustevalA structured overview of all examples are now available on the documentation pages.
Please cite clusteval in your publications if this is useful for your research (see right top for citation).
- Use ARI when the ground truth clustering has large equal sized clusters
- Usa AMI when the ground truth clustering is unbalanced and there exist small clusters
- https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html
- https://scikit-learn.org/stable/auto_examples/cluster/plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-py
- https://github.com/idealo/imagededup
- https://towardsdatascience.com/how-to-cluster-images-based-on-visual-similarity-cd6e7209fe34
- https://github.com/facebookresearch/deepcluster
- https://towardsdatascience.com/pca-on-hyperspectral-data-99c9c5178385
- https://machinelearningmastery.com/face-recognition-using-principal-component-analysis/