{"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/git-clustering-based-on-graph-of-intensity","title":"Git: Clustering Based on Graph of Intensity Topology","arxiv_id":"2110.01274","date":"2021-10-04","proceeding":null,"authors":["Zhangyang Gao","Haitao Lin","Cheng Tan","Lirong Wu","Stan. Z Li"],"abstract":"\\textbf{A}ccuracy, \\textbf{R}obustness to noises and scales, \\textbf{I}nterpretability, \\textbf{S}peed, and \\textbf{E}asy to use (ARISE) are crucial requirements of a good clustering algorithm. However, achieving these goals simultaneously is challenging, and most advanced approaches only focus on parts of them. Towards an overall consideration of these aspects, we propose a novel clustering algorithm, namely GIT (Clustering Based on \\textbf{G}raph of \\textbf{I}ntensity \\textbf{T}opology). GIT considers both local and global data structures: firstly forming local clusters based on intensity peaks of samples, and then estimating the global topological graph (topo-graph) between these local clusters. We use the Wasserstein Distance between the predicted and prior class proportions to automatically cut noisy edges in the topo-graph and merge connected local clusters as final clusters. Then, we compare GIT with seven competing algorithms on five synthetic datasets and nine real-world datasets. With fast local cluster detection, robust topo-graph construction and accurate edge-cutting, GIT shows attractive ARISE performance and significantly exceeds other non-convex clustering methods. For example, GIT outperforms its counterparts about $10\\%$ (F1-score) on MNIST and FashionMNIST. Code is available at \\color{red}{https://github.com/gaozhangyang/GIT}.","url_abs":"https://arxiv.org/abs/2110.01274v1","url_pdf":"https://arxiv.org/pdf/2110.01274v1.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":"git-clustering-based-on-graph-of-intensity","repo_url":"https://github.com/gaozhangyang/git","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"git-clustering-based-on-graph-of-intensity","repo_url":"https://github.com/gaozhangyang/DGC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"git-clustering-based-on-graph-of-intensity","repo_url":"https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/git","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"git-clustering-based-on-graph-of-intensity","repo_url":"https://github.com/yangyucheng000/University/tree/main/model-2/git","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"clustering-algorithms-evaluation","task_name":"Clustering Algorithms Evaluation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/clustering-algorithms-evaluation-on-fashion-2","task":"Clustering Algorithms Evaluation","dataset":"Fashion-MNIST","model":"AE+GIT","rank_in_archive_order":1,"of":6,"metrics":{"ARI":"49%","F1-score":"65%","NMI":"61%"},"uses_additional_data":false},{"leaderboard":"/sota/clustering-algorithms-evaluation-on-fashion-2","task":"Clustering Algorithms Evaluation","dataset":"Fashion-MNIST","model":"k-Means++","rank_in_archive_order":2,"of":6,"metrics":{"ARI":"35%","F1-score":"39%","NMI":"51%"},"uses_additional_data":false},{"leaderboard":"/sota/clustering-algorithms-evaluation-on-fashion-2","task":"Clustering Algorithms Evaluation","dataset":"Fashion-MNIST","model":"Spectral 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