hdbscan: Hierarchical density based clustering

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hdbscan: Hierarchical density based clustering

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26 February 2017. Paper DOI: http://dx.doi.org/10.21105/joss.00205. Software Repository: https://github.com/scikit-learn-contrib/hdbscan. Software Archive: ...

hdbscan: Hierarchical density based clustering Leland McInnes1 , John Healy1 , and Steve Astels2 1

Tutte Institute for Mathematics and Computing 2 Shopify 26 February 2017

Paper DOI: http://dx.doi.org/10.21105/joss.00205 Software Repository: https://github.com/scikit-learn-contrib/hdbscan Software Archive: http://dx.doi.org/10.5281/zenodo.401403

Summary HDBSCAN: Hierarchical Density-Based Spatial Clustering of Applications with Noise (Campello, Moulavi, and Sander 2013), (Campello et al. 2015). 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. The library also includes support for Robust Single Linkage clustering (Chaudhuri et al. 2014), (Chaudhuri and Dasgupta 2010), GLOSH outlier detection (Campello et al. 2015), and tools for visualizing and exploring cluster structures. Finally support for prediction and soft clustering is also available.

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References Campello, Ricardo JGB, Davoud Moulavi, and Joerg Sander. 2013. “Density-Based Clustering Based on Hierarchical Density Estimates.” In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 160–72. Springer. doi:10.1007/978-3-642-37456-2_14. Campello, Ricardo JGB, Davoud Moulavi, Arthur Zimek, and Jörg Sander. 2015. “Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection.” ACM Transactions on Knowledge Discovery from Data (TKDD) 10 (1). ACM: 5. doi:10.1145/2733381. Chaudhuri, Kamalika, and Sanjoy Dasgupta. 2010. “Rates of Convergence for the Cluster Tree.” In Proceedings of the 23rd International Conference on Neural Information Processing Systems, 343–51. NIPS’10. USA: Curran Associates Inc. https://papers.nips.cc/paper/4068-rates-of-convergence-for-the-cluster-tree. Chaudhuri, Kamalika, Sanjoy Dasgupta, Samory Kpotufe, and Ulrike von Luxburg. 2014. “Consistent Procedures for Cluster Tree Estimation and Pruning.” IEEE Transactions on Information Theory 60 (12). IEEE: 7900–7912. doi:10.1109/TIT.2014.2361055.

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