{"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/graph-degree-linkage-agglomerative-clustering","title":"Graph Degree Linkage: Agglomerative Clustering on a Directed Graph","arxiv_id":"1208.5092","date":"2012-08-25","proceeding":null,"authors":["Wei Zhang","Xiaogang Wang","Deli Zhao","Xiaoou Tang"],"abstract":"This paper proposes a simple but effective graph-based agglomerative\nalgorithm, for clustering high-dimensional data. We explore the different roles\nof two fundamental concepts in graph theory, indegree and outdegree, in the\ncontext of clustering. The average indegree reflects the density near a sample,\nand the average outdegree characterizes the local geometry around a sample.\nBased on such insights, we define the affinity measure of clusters via the\nproduct of average indegree and average outdegree. The product-based affinity\nmakes our algorithm robust to noise. The algorithm has three main advantages:\ngood performance, easy implementation, and high computational efficiency. We\ntest the algorithm on two fundamental computer vision problems: image\nclustering and object matching. Extensive experiments demonstrate that it\noutperforms the state-of-the-arts in both applications.","url_abs":"http://arxiv.org/abs/1208.5092v1","url_pdf":"http://arxiv.org/pdf/1208.5092v1.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":"graph-degree-linkage-agglomerative-clustering","repo_url":"https://github.com/waynezhanghk/gacluster","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"graph-degree-linkage-agglomerative-clustering","repo_url":"https://github.com/waynezhanghk/gactoolbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"image-clustering","task_name":"Image Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-clustering-on-coil-20","task":"Image Clustering","dataset":"Coil-20","model":"AGDL","rank_in_archive_order":3,"of":6,"metrics":{"Accuracy":"0.858","NMI":"0.937"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-coil-20","task":"Image Clustering","dataset":"Coil-20","model":"GDL-U","rank_in_archive_order":5,"of":6,"metrics":{"NMI":"0.746"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-coil-20","task":"Image Clustering","dataset":"Coil-20","model":"GDL","rank_in_archive_order":6,"of":6,"metrics":{"Accuracy":"0.858"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-extended-yale-b","task":"Image Clustering","dataset":"Extended Yale-B","model":"GDL-U","rank_in_archive_order":8,"of":9,"metrics":{"NMI":"0.91"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-extended-yale-b","task":"Image Clustering","dataset":"Extended Yale-B","model":"AGDL","rank_in_archive_order":9,"of":9,"metrics":{"NMI":"0.91"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-fashion-mnist","task":"Image Clustering","dataset":"Fashion-MNIST","model":"GDL","rank_in_archive_order":8,"of":13,"metrics":{"Accuracy":"0.627","NMI":"0.66"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-mnist-full","task":"Image Clustering","dataset":"MNIST-full","model":"GDL","rank_in_archive_order":14,"of":16,"metrics":{"Accuracy":"0.965","NMI":"0.913"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-mnist-test","task":"Image Clustering","dataset":"MNIST-test","model":"GDL","rank_in_archive_order":7,"of":11,"metrics":{"NMI":"0.91"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-mnist-test","task":"Image Clustering","dataset":"MNIST-test","model":"AGDL","rank_in_archive_order":11,"of":11,"metrics":{"NMI":"0.844"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-usps","task":"Image Clustering","dataset":"USPS","model":"AGDL","rank_in_archive_order":15,"of":16,"metrics":{"NMI":"0.824"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-coil-100","task":"Image Clustering","dataset":"coil-100","model":"GDL-U","rank_in_archive_order":6,"of":10,"metrics":{"NMI":"0.929"},"uses_additional_data":false},{"leaderboard":"/sota/image-clustering-on-coil-100","task":"Image Clustering","dataset":"coil-100","model":"GDL","rank_in_archive_order":9,"of":10,"metrics":{"Accuracy":"0.731"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1208.5092","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}