{"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/scalable-k-means-clustering-for-large-k-via","title":"Scalable k-Means Clustering for Large k via Seeded Approximate Nearest-Neighbor Search","arxiv_id":"2502.06163","date":"2025-02-10","proceeding":null,"authors":["Jack Spalding-Jamieson","Eliot Wong Robson","Da Wei Zheng"],"abstract":"For very large values of $k$, we consider methods for fast $k$-means clustering of massive datasets with $10^7\\sim10^9$ points in high-dimensions ($d\\geq100$). All current practical methods for this problem have runtimes at least $\\Omega(k^2)$. We find that initialization routines are not a bottleneck for this case. Instead, it is critical to improve the speed of Lloyd's local-search algorithm, particularly the step that reassigns points to their closest center. Attempting to improve this step naturally leads us to leverage approximate nearest-neighbor search methods, although this alone is not enough to be practical. Instead, we propose a family of problems we call \"Seeded Approximate Nearest-Neighbor Search\", for which we propose \"Seeded Search-Graph\" methods as a solution.","url_abs":"https://arxiv.org/abs/2502.06163v1","url_pdf":"https://arxiv.org/pdf/2502.06163v1.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":"scalable-k-means-clustering-for-large-k-via","repo_url":"https://github.com/jacketsj/mopbucket","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"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}