{"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/particle-clustering-machine-a-dynamical","title":"Particle Clustering Machine: A Dynamical System Based Approach","arxiv_id":"1801.01017","date":"2017-12-30","proceeding":null,"authors":["Sambarta Dasgupta","Keivan Ebrahimi","Umesh Vaidya"],"abstract":"Identification of the clusters from an unlabeled data set is one of the most\nimportant problems in Unsupervised Machine Learning. The state of the art\nclustering algorithms are based on either the statistical properties or the\ngeometric properties of the data set. In this work, we propose a novel method\nto cluster the data points using dynamical systems theory. After constructing a\ngradient dynamical system using interaction potential, we prove that the\nasymptotic dynamics of this system will determine the cluster centers, when the\ndynamical system is initialized at the data points. Most of the existing\nheuristic-based clustering techniques suffer from a disadvantage, namely the\nstochastic nature of the solution. Whereas, the proposed algorithm is\ndeterministic, and the outcome would not change over multiple runs of the\nproposed algorithm with the same input data. Another advantage of the proposed\nmethod is that the number of clusters, which is difficult to determine in\npractice, does not have to be specified in advance. Simulation results with are\npresented, and comparisons are made with the existing methods.","url_abs":"http://arxiv.org/abs/1801.01017v1","url_pdf":"http://arxiv.org/pdf/1801.01017v1.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":"particle-clustering-machine-a-dynamical","repo_url":"https://github.com/yyll008/yyll008.github.io","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","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}