{"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/pyupmask-an-improved-unsupervised-clustering","title":"pyUPMASK: an improved unsupervised clustering algorithm","arxiv_id":"2101.01660","date":"2021-01-05","proceeding":null,"authors":["M. S. Pera","G. I. Perren","A. Moitinho","H. D. Navone","R. A. Vazquez"],"abstract":"Aims. We present pyUPMASK, an unsupervised clustering method for stellar clusters that builds upon the original UPMASK package. Its general approach makes it plausible to be applied to analyses that deal with binary classes of any kind, as long as the fundamental hypotheses are met. The code is written entirely in Python and is made available through a public repository. Methods.The core of the algorithm follows the method developed in UPMASK but introducing several key enhancements. These enhancements not only make pyUPMASK more general, they also improve its performance considerably. Results. We thoroughly tested the performance of pyUPMASK on 600 synthetic clusters, affected by varying degrees of contamination by field stars. To assess the performance we employed six different statistical metrics that measure the accuracy of probabilistic classification. Conclusions. Our results show that pyUPMASK is better performant than UPMASK for every statistical performance metric, while still managing to be many times faster.","url_abs":"https://arxiv.org/abs/2101.01660v3","url_pdf":"https://arxiv.org/pdf/2101.01660v3.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":"pyupmask-an-improved-unsupervised-clustering","repo_url":"https://github.com/msolpera/pyUPMASK","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}