{"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/dynamic-clustering-via-asymptotics-of-the","title":"Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture","arxiv_id":"1305.6659","date":"2013-05-28","proceeding":"NeurIPS 2013 12","authors":["Trevor Campbell","Miao Liu","Brian Kulis","Jonathan P. How","Lawrence Carin"],"abstract":"This paper presents a novel algorithm, based upon the dependent Dirichlet\nprocess mixture model (DDPMM), for clustering batch-sequential data containing\nan unknown number of evolving clusters. The algorithm is derived via a\nlow-variance asymptotic analysis of the Gibbs sampling algorithm for the DDPMM,\nand provides a hard clustering with convergence guarantees similar to those of\nthe k-means algorithm. Empirical results from a synthetic test with moving\nGaussian clusters and a test with real ADS-B aircraft trajectory data\ndemonstrate that the algorithm requires orders of magnitude less computational\ntime than contemporary probabilistic and hard clustering algorithms, while\nproviding higher accuracy on the examined datasets.","url_abs":"http://arxiv.org/abs/1305.6659v2","url_pdf":"http://arxiv.org/pdf/1305.6659v2.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":"dynamic-clustering-via-asymptotics-of-the","repo_url":"https://github.com/trevorcampbell/dynamic-means","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"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}