{"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/discrete-optimal-graph-clustering","title":"Discrete Optimal Graph Clustering","arxiv_id":"1904.11266","date":"2019-04-25","proceeding":null,"authors":["Yudong Han","Lei Zhu","Zhiyong Cheng","Jingjing Li","Xiaobai Liu"],"abstract":"Graph based clustering is one of the major clustering methods. Most of it\nwork in three separate steps: similarity graph construction, clustering label\nrelaxing and label discretization with k-means. Such common practice has three\ndisadvantages: 1) the predefined similarity graph is often fixed and may not be\noptimal for the subsequent clustering. 2) the relaxing process of cluster\nlabels may cause significant information loss. 3) label discretization may\ndeviate from the real clustering result since k-means is sensitive to the\ninitialization of cluster centroids. To tackle these problems, in this paper,\nwe propose an effective discrete optimal graph clustering (DOGC) framework. A\nstructured similarity graph that is theoretically optimal for clustering\nperformance is adaptively learned with a guidance of reasonable rank\nconstraint. Besides, to avoid the information loss, we explicitly enforce a\ndiscrete transformation on the intermediate continuous label, which derives a\ntractable optimization problem with discrete solution. Further, to compensate\nthe unreliability of the learned labels and enhance the clustering accuracy, we\ndesign an adaptive robust module that learns prediction function for the unseen\ndata based on the learned discrete cluster labels. Finally, an iterative\noptimization strategy guaranteed with convergence is developed to directly\nsolve the clustering results. Extensive experiments conducted on both real and\nsynthetic datasets demonstrate the superiority of our proposed methods compared\nwith several state-of-the-art clustering approaches.","url_abs":"http://arxiv.org/abs/1904.11266v1","url_pdf":"http://arxiv.org/pdf/1904.11266v1.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":"discrete-optimal-graph-clustering","repo_url":"https://github.com/christinecui/DOGC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"graph-clustering","task_name":"Graph 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}