{"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/a-probabilistic-framework-for-quantum","title":"A Probabilistic framework for Quantum Clustering","arxiv_id":"1902.05578","date":"2019-02-14","proceeding":null,"authors":["Raúl V. Casaña-Eslava","Paulo J. G. Lisboa","Sandra Ortega-Martorell","Ian H. Jarman","José D. Martín-Guerrero"],"abstract":"Quantum Clustering is a powerful method to detect clusters in data with mixed\ndensity. However, it is very sensitive to a length parameter that is inherent\nto the Schr\\\"odinger equation. In addition, linking data points into clusters\nrequires local estimates of covariance that are also controlled by length\nparameters. This raises the question of how to adjust the control parameters of\nthe Schr\\\"odinger equation for optimal clustering. We propose a probabilistic\nframework that provides an objective function for the goodness-of-fit to the\ndata, enabling the control parameters to be optimised within a Bayesian\nframework. This naturally yields probabilities of cluster membership and data\npartitions with specific numbers of clusters. The proposed framework is tested\non real and synthetic data sets, assessing its validity by measuring\nconcordance with known data structure by means of the Jaccard score (JS). This\nwork also proposes an objective way to measure performance in unsupervised\nlearning that correlates very well with JS.","url_abs":"http://arxiv.org/abs/1902.05578v1","url_pdf":"http://arxiv.org/pdf/1902.05578v1.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":"a-probabilistic-framework-for-quantum","repo_url":"https://github.com/racaes/PQC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","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}