{"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/autonomous-clustering-by-fast-find-of-mass","title":"Autonomous clustering by fast find of mass and distance peaks","arxiv_id":null,"date":"2024-05-13","proceeding":"techrxiv 2024 5","authors":["Jie Yang","Chin-Teng Lin","University of Technology Sydney"],"abstract":"Clustering is a fundamental tool of scientific analysis, ubiquitous in disciplines from biology and chemistry to astronomy and\r\npattern recognition. We propose a novel clustering algorithm based on the natural idea that a cluster and its nearest neighbor\r\nwith higher mass should be merged into one cluster, unless they both have relatively large masses and the distance between\r\nthem is also relatively large. The find of mass and distance peaks reveals the mergers that don’t conform to the rule and should\r\nbe removed. The algorithm is parameter-free and harnesses this idea to recognize any cluster and find the proper number of\r\nclusters and noise autonomously. Experiments on numerous synthetic and real-world data sets show the enormous versatility\r\nof the proposed algorithm that remarkably outperforms the best compared algorithm. Additionally, we also compare it with\r\nlatest state-of-the-art deep clustering algorithms on several challenging image data sets. The proposed algorithm without any\r\ndeep representation achieves better or close performance than deep clustering algorithms on image clustering.","url_abs":"https://www.techrxiv.org/users/686426/articles/679723-autonomous-clustering-by-fast-find-of-mass-and-distance-peaks","url_pdf":"https://d197for5662m48.cloudfront.net/documents/publicationstatus/207698/preprint_pdf/aeba0023717467e1e1276fad6a2b5fab.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":"autonomous-clustering-by-fast-find-of-mass","repo_url":"https://github.com/JieYangBruce/TorqueClustering","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"astronomy","task_name":"Astronomy"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"deep-clustering","task_name":"Deep Clustering"},{"task_slug":"image-clustering","task_name":"Image Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}