{"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/efficient-clustering-based-on-a-unified-view","title":"Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut","arxiv_id":null,"date":"2020-12-01","proceeding":"NeurIPS 2020 12","authors":["Shenfei Pei","Feiping Nie","Rong Wang","Xuelong Li"],"abstract":"Spectral clustering and $k$-means, both as two major traditional clustering methods, are still attracting a lot of attention, although a variety of novel clustering algorithms have been proposed in recent years.\nFirstly, a unified framework of $k$-means and ratio-cut is revisited,  and a novel and efficient clustering algorithm is then proposed based on this framework. \nThe time and space complexity of our method are both linear with respect to the number of samples, and are independent of the number of clusters to construct, more importantly.\nThese properties mean that it is easily scalable and applicable to large practical problems.\nExtensive experiments on 12 real-world benchmark and 8 facial datasets validate the advantages of the proposed algorithms compared to the state-of-the-art clustering algorithms. \nIn particular, over 15x and 7x speed-up can be obtained with respect to $k$-means on the synthetic dataset of 1 million samples and the benchmark dataset (CelebA) of 200k samples, respectively [GitHub].","url_abs":"http://proceedings.neurips.cc/paper/2020/hash/aa108f56a10e75c1f20f27723ecac85f-Abstract.html","url_pdf":"http://proceedings.neurips.cc/paper/2020/file/aa108f56a10e75c1f20f27723ecac85f-Paper.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":"efficient-clustering-based-on-a-unified-view","repo_url":"https://github.com/ShenfeiPei/KSUMS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}