HDBSCAN* is an easy-to-use, almost parameterless framework for unsupervised and semi-supervised descriptive data analysis based on hierarchical density estimates. Developed in collaboration with the creator of DBSCAN, OPTICS, and LOF, Prof. Jörg Sander, this tool allows state-of-the-art hierarchical density-based clustering, noise modeling, unsupervised and semi-supervised cluster extraction (flat clustering from flexible, non-horizontal dendrogram cuts), outlier detection, as well as different forms of visualization.
CLICK HERE to download the complete Java Package with both source and executable files.
NOTE: please, refer to our ACM TKDD paper when referencing HDBSCAN*:
R.J.G.B. Campello, D. Moulavi, A. Zimek and J. Sander “Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection”, ACM Trans. on Knowledge Discovery from Data, Vol 10, 1 (July 2015), 1-51.