{"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/modern-hierarchical-agglomerative-clustering","title":"Modern hierarchical, agglomerative clustering algorithms","arxiv_id":"1109.2378","date":"2011-09-12","proceeding":null,"authors":["Daniel Müllner"],"abstract":"This paper presents algorithms for hierarchical, agglomerative clustering\nwhich perform most efficiently in the general-purpose setup that is given in\nmodern standard software. Requirements are: (1) the input data is given by\npairwise dissimilarities between data points, but extensions to vector data are\nalso discussed (2) the output is a \"stepwise dendrogram\", a data structure\nwhich is shared by all implementations in current standard software. We present\nalgorithms (old and new) which perform clustering in this setting efficiently,\nboth in an asymptotic worst-case analysis and from a practical point of view.\nThe main contributions of this paper are: (1) We present a new algorithm which\nis suitable for any distance update scheme and performs significantly better\nthan the existing algorithms. (2) We prove the correctness of two algorithms by\nRohlf and Murtagh, which is necessary in each case for different reasons. (3)\nWe give well-founded recommendations for the best current algorithms for the\nvarious agglomerative clustering schemes.","url_abs":"http://arxiv.org/abs/1109.2378v1","url_pdf":"http://arxiv.org/pdf/1109.2378v1.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":"modern-hierarchical-agglomerative-clustering","repo_url":"https://github.com/UCLOrengoGroup/cath-tools","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"modern-hierarchical-agglomerative-clustering","repo_url":"https://github.com/elki-project/elki","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1109.2378","atlas_url":"https://app.syntology.ai/?focus=1109.2378","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}