{"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-sequential-spectral-clustering","title":"Scalable Sequential Spectral Clustering","arxiv_id":null,"date":"2016-02-12","proceeding":"AAAI 2016 2","authors":["Yeqing Li","Junzhou Huang","Wei Liu"],"abstract":"In the past decades, Spectral Clustering (SC) has become one\r\nof the most effective clustering approaches. Although it has\r\nbeen widely used, one significant drawback of SC is its expensive computation cost. Many efforts have been devoted to\r\naccelerating SC algorithms and promising results have been\r\nachieved. However, most of the existing algorithms rely on\r\nthe assumption that data can be stored in the computer memory. When data cannot fit in the memory, these algorithms\r\nwill suffer severe performance degradations. In order to overcome this issue, we propose a novel sequential SC algorithm\r\nfor tackling large-scale clustering with limited computational\r\nresources, e.g., memory. We begin with investigating an effective way of approximating the graph affinity matrix via\r\nleveraging a bipartite graph. Then we choose a smart graph\r\nconstruction and optimization strategy to avoid random access to data. These efforts lead to an efficient SC algorithm\r\nwhose memory usage is independent of the number of input data points. Extensive experiments carried out on large\r\ndatasets demonstrate that the proposed sequential SC algorithm is up to a thousand times faster than the state-of-thearts.","url_abs":"https://ojs.aaai.org/index.php/AAAI/article/view/10298/10157","url_pdf":"https://ojs.aaai.org/index.php/AAAI/article/view/10298/10157","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-sequential-spectral-clustering","repo_url":"https://github.com/zahraDehghanian97/Scalable_Sequential_Spectral_Clustering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"image-clustering","task_name":"Image Clustering"},{"task_slug":"graph-construction","task_name":"graph construction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}