{"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/adaptive-nonparametric-clustering","title":"Adaptive Nonparametric Clustering","arxiv_id":"1709.09102","date":"2017-09-26","proceeding":null,"authors":["Kirill Efimov","Larisa Adamyan","Vladimir Spokoiny"],"abstract":"This paper presents a new approach to non-parametric cluster analysis called\nAdaptive Weights Clustering (AWC). The idea is to identify the clustering\nstructure by checking at different points and for different scales on departure\nfrom local homogeneity. The proposed procedure describes the clustering\nstructure in terms of weights \\( w_{ij} \\) each of them measures the degree of\nlocal inhomogeneity for two neighbor local clusters using statistical tests of\n\"no gap\" between them. % The procedure starts from very local scale, then the\nparameter of locality grows by some factor at each step. The method is fully\nadaptive and does not require to specify the number of clusters or their\nstructure. The clustering results are not sensitive to noise and outliers, the\nprocedure is able to recover different clusters with sharp edges or manifold\nstructure. The method is scalable and computationally feasible. An intensive\nnumerical study shows a state-of-the-art performance of the method in various\nartificial examples and applications to text data. Our theoretical study states\noptimal sensitivity of AWC to local inhomogeneity.","url_abs":"http://arxiv.org/abs/1709.09102v1","url_pdf":"http://arxiv.org/pdf/1709.09102v1.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":"adaptive-nonparametric-clustering","repo_url":"https://github.com/larisahax/awc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"nonparametric-clustering","task_name":"Nonparametric 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}