{"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/divide-and-conquer-based-large-scale-spectral-1","title":"Divide-and-conquer based Large-Scale Spectral Clustering","arxiv_id":"2104.15042","date":"2021-04-30","proceeding":null,"authors":["Hongmin Li","Xiucai Ye","Akira Imakura","Tetsuya Sakurai"],"abstract":"Spectral clustering is one of the most popular clustering methods. However, how to balance the efficiency and effectiveness of the large-scale spectral clustering with limited computing resources has not been properly solved for a long time. In this paper, we propose a divide-and-conquer based large-scale spectral clustering method to strike a good balance between efficiency and effectiveness. In the proposed method, a divide-and-conquer based landmark selection algorithm and a novel approximate similarity matrix approach are designed to construct a sparse similarity matrix within low computational complexities. Then clustering results can be computed quickly through a bipartite graph partition process. The proposed method achieves a lower computational complexity than most existing large-scale spectral clustering methods. Experimental results on ten large-scale datasets have demonstrated the efficiency and effectiveness of the proposed method. The MATLAB code of the proposed method and experimental datasets are available at https://github.com/Li-Hongmin/MyPaperWithCode.","url_abs":"https://arxiv.org/abs/2104.15042v2","url_pdf":"https://arxiv.org/pdf/2104.15042v2.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":"divide-and-conquer-based-large-scale-spectral-1","repo_url":"https://github.com/Li-Hongmin/MyPaperWithCode","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"imagedocument-clustering","task_name":"Image/Document Clustering"}],"methods":[{"method_slug":"spectral-clustering","method_name":"Spectral Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-document-clustering-on-pendigits","task":"Image/Document Clustering","dataset":"pendigits","model":"LSC-R","rank_in_archive_order":2,"of":7,"metrics":{"Accuracy (%)":"81.55","NMI":"79.15","runtime (s)":"0.77"},"uses_additional_data":false},{"leaderboard":"/sota/image-document-clustering-on-pendigits","task":"Image/Document Clustering","dataset":"pendigits","model":"LSC-K","rank_in_archive_order":4,"of":7,"metrics":{"Accuracy (%)":"74.02","NMI":"81.37","runtime (s)":"1.20"},"uses_additional_data":false},{"leaderboard":"/sota/image-document-clustering-on-pendigits","task":"Image/Document Clustering","dataset":"pendigits","model":"U-SPEC","rank_in_archive_order":6,"of":7,"metrics":{"Accuracy (%)":"81.68","NMI":"81.68","runtime (s)":"2.07"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}