{"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/powerful-significance-testing-for-unbalanced","title":"Powerful Significance Testing for Unbalanced Clusters","arxiv_id":"2308.13079","date":"2023-08-24","proceeding":null,"authors":["Thomas H. Keefe","J. S. Marron"],"abstract":"Clustering methods are popular for revealing structure in data, particularly in the high-dimensional setting common to contemporary data science. A central statistical question is, \"are the clusters really there?\" One pioneering method in statistical cluster validation is SigClust, but it is severely underpowered in the important setting where the candidate clusters have unbalanced sizes, such as in rare subtypes of disease. We show why this is the case, and propose a remedy that is powerful in both the unbalanced and balanced settings, using a novel generalization of k-means clustering. We illustrate the value of our method using a high-dimensional dataset of gene expression in kidney cancer patients. A Python implementation is available at https://github.com/thomaskeefe/sigclust.","url_abs":"https://arxiv.org/abs/2308.13079v1","url_pdf":"https://arxiv.org/pdf/2308.13079v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"powerful-significance-testing-for-unbalanced","repo_url":"https://github.com/thomaskeefe/sigclust","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}