{"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/eigenevent-an-algorithm-for-event-detection","title":"EigenEvent: An Algorithm for Event Detection from Complex Data Streams in Syndromic Surveillance","arxiv_id":"1406.3496","date":"2014-06-13","proceeding":null,"authors":["Hadi Fanaee-T","João Gama"],"abstract":"Syndromic surveillance systems continuously monitor multiple pre-diagnostic\ndaily streams of indicators from different regions with the aim of early\ndetection of disease outbreaks. The main objective of these systems is to\ndetect outbreaks hours or days before the clinical and laboratory confirmation.\nThe type of data that is being generated via these systems is usually\nmultivariate and seasonal with spatial and temporal dimensions. The algorithm\nWhat's Strange About Recent Events (WSARE) is the state-of-the-art method for\nsuch problems. It exhaustively searches for contrast sets in the multivariate\ndata and signals an alarm when find statistically significant rules. This\nbottom-up approach presents a much lower detection delay comparing the existing\ntop-down approaches. However, WSARE is very sensitive to the small-scale\nchanges and subsequently comes with a relatively high rate of false alarms. We\npropose a new approach called EigenEvent that is neither fully top-down nor\nbottom-up. In this method, we instead of top-down or bottom-up search, track\nchanges in data correlation structure via eigenspace techniques. This new\nmethodology enables us to detect both overall changes (via eigenvalue) and\ndimension-level changes (via eigenvectors). Experimental results on hundred\nsets of benchmark data reveals that EigenEvent presents a better overall\nperformance comparing state-of-the-art, in particular in terms of the false\nalarm rate.","url_abs":"http://arxiv.org/abs/1406.3496v1","url_pdf":"http://arxiv.org/pdf/1406.3496v1.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":"eigenevent-an-algorithm-for-event-detection","repo_url":"https://github.com/fanaee/EigenEvent","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"event-detection","task_name":"Event Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}