{"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/a-binary-optimization-approach-for","title":"A Binary Optimization Approach for Constrained K-Means Clustering","arxiv_id":"1810.10134","date":"2018-10-24","proceeding":null,"authors":["Huu Le","Anders Eriksson","Thanh-Toan Do","Michael Milford"],"abstract":"K-Means clustering still plays an important role in many computer vision\nproblems. While the conventional Lloyd method, which alternates between\ncentroid update and cluster assignment, is primarily used in practice, it may\nconverge to a solution with empty clusters. Furthermore, some applications may\nrequire the clusters to satisfy a specific set of constraints, e.g., cluster\nsizes, must-link/cannot-link. Several methods have been introduced to solve\nconstrained K-Means clustering. Due to the non-convex nature of K-Means,\nhowever, existing approaches may result in sub-optimal solutions that poorly\napproximate the true clusters. In this work, we provide a new perspective to\ntackle this problem. Particularly, we reconsider constrained K-Means as a\nBinary Optimization Problem and propose a novel optimization scheme to search\nfor feasible solutions in the binary domain. This approach allows us to solve\nconstrained K-Means where multiple types of constraints can be simultaneously\nenforced. Experimental results on synthetic and real datasets show that our\nmethod provides better clustering accuracy with faster runtime compared to\nseveral commonly used techniques.","url_abs":"http://arxiv.org/abs/1810.10134v2","url_pdf":"http://arxiv.org/pdf/1810.10134v2.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":"a-binary-optimization-approach-for","repo_url":"https://github.com/intellhave/BCKM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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}