{"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/gravitational-clustering","title":"Gravitational Clustering","arxiv_id":"1509.01659","date":"2015-09-05","proceeding":null,"authors":["Armen Aghajanyan"],"abstract":"The downfall of many supervised learning algorithms, such as neural networks,\nis the inherent need for a large amount of training data. Although there is a\nlot of buzz about big data, there is still the problem of doing classification\nfrom a small dataset. Other methods such as support vector machines, although\ncapable of dealing with few samples, are inherently binary classifiers, and are\nin need of learning strategies such as One vs All in the case of\nmulti-classification. In the presence of a large number of classes this can\nbecome problematic. In this paper we present, a novel approach to supervised\nlearning through the method of clustering. Unlike traditional methods such as\nK-Means, Gravitational Clustering does not require the initial number of\nclusters, and automatically builds the clusters, individual samples can be\narbitrarily weighted and it requires only few samples while staying resilient\nto over-fitting.","url_abs":"http://arxiv.org/abs/1509.01659v1","url_pdf":"http://arxiv.org/pdf/1509.01659v1.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":"gravitational-clustering","repo_url":"https://github.com/ArmenAg/GravitationalClustering","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}