swMATH ID: 39421
Software Authors: Leland McInnes; John Healy; Steve Astels
Description: 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).
Homepage: https://hdbscan.readthedocs.io/en/latest/
Source Code: https://github.com/scikit-learn-contrib/hdbscan
Keywords: hdbscan; Hierarchical density based clustering; Journal of Open Source Software
Related Software: Scikit; UMAP; Python; UCI-ml; PyTorch; sf; tessagon; VoronoiDelaunay.jl; bleiglas; esda; libpysal; Pysal; OpenStreetMap; mercantile; GeoPandas; h3-py; Shapely; momepy; OSMnx; NURBS-Python
Cited in: 6 Publications

Citations by Year