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).
WWW: https://github.com/scikit-learn-contrib/hdbscan
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