hdbscan 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 all top 5 Cited by 15 Authors 1 Ali, Bahar Obed 1 Azam, Nouman 1 Bonasera, Stefano 1 Bosanac, Natasha 1 d’Errico, Maria 1 Facco, Elena 1 Gebbie, Tim 1 Laio, Alessandro 1 Lin, Yu 1 Rodriguez, Alex D. 1 Shah, Anwar 1 Song, Anna 1 Wickramarachchi, Anuradha 1 Yao, JingTao 1 Yelibi, Lionel Cited in 5 Serials 1 Information Sciences 1 International Journal of Approximate Reasoning 1 Celestial Mechanics and Dynamical Astronomy 1 Journal of Mathematical Imaging and Vision 1 Journal of Statistical Mechanics: Theory and Experiment all top 5 Cited in 6 Fields 2 Statistics (62-XX) 2 Computer science (68-XX) 1 Mechanics of particles and systems (70-XX) 1 Statistical mechanics, structure of matter (82-XX) 1 Biology and other natural sciences (92-XX) 1 Information and communication theory, circuits (94-XX) Citations by Year