HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that extends DBSCAN by performing DBSCAN over a range of epsilon values and integrating the results to find the most stable clustering. This approach allows HDBSCAN to identify clusters of varying densities, making it more robust to parameter selection compared to DBSCAN. The library also supports Robust Single Linkage clustering, GLOSH outlier detection, and tools for visualizing and exploring cluster structures. Additionally, it offers prediction and soft clustering capabilities. The authors of the JOSS paper retain the copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY).HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that extends DBSCAN by performing DBSCAN over a range of epsilon values and integrating the results to find the most stable clustering. This approach allows HDBSCAN to identify clusters of varying densities, making it more robust to parameter selection compared to DBSCAN. The library also supports Robust Single Linkage clustering, GLOSH outlier detection, and tools for visualizing and exploring cluster structures. Additionally, it offers prediction and soft clustering capabilities. The authors of the JOSS paper retain the copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY).