{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/accelerated-hierarchical-density-clustering","title":"Accelerated Hierarchical Density Clustering","arxiv_id":"1705.07321","date":"2017-05-20","proceeding":null,"authors":["Leland McInnes","John Healy"],"abstract":"We present an accelerated algorithm for hierarchical density based\nclustering. Our new algorithm improves upon HDBSCAN*, which itself provided a\nsignificant qualitative improvement over the popular DBSCAN algorithm. The\naccelerated HDBSCAN* algorithm provides comparable performance to DBSCAN, while\nsupporting variable density clusters, and eliminating the need for the\ndifficult to tune distance scale parameter. This makes accelerated HDBSCAN* the\ndefault choice for density based clustering.\n  Library available at: https://github.com/scikit-learn-contrib/hdbscan","url_abs":"http://arxiv.org/abs/1705.07321v2","url_pdf":"http://arxiv.org/pdf/1705.07321v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"accelerated-hierarchical-density-clustering","repo_url":"https://github.com/scikit-learn-contrib/hdbscan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"accelerated-hierarchical-density-clustering","repo_url":"https://github.com/jeesaugustine/metric-space-proximity-algo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"accelerated-hierarchical-density-clustering","repo_url":"https://github.com/sigmod-2021-opti-metric-space-proxy/broad-general-metric-distance-opti","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.07321","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}