GitHub - scikit-learn-contrib/hdbscan: A high performance implementation of HDBSCAN clustering.

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 s

[kenmatsu4]

“import hdbscan from sklearn.datasets import make_blobs data, _ = make_blobs(1000) clusterer = hdbscan.HDBSCAN(min_cluster_size=10) cluster_labels = clusterer.fit_predict(data)”