{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:28:19Z","timestamp":1750307299009,"version":"3.41.0"},"reference-count":16,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2010,10,1]],"date-time":"2010-10-01T00:00:00Z","timestamp":1285891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["612170"],"award-info":[{"award-number":["612170"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2010,10]]},"abstract":"<jats:p>\n            Given a spatial dataset placed on an\n            <jats:italic>n<\/jats:italic>\n            \u00d7\n            <jats:italic>n<\/jats:italic>\n            grid, our goal is to find the rectangular regions within which subsets of the dataset exhibit anomalous behavior. We develop algorithms that, given any user-supplied arbitrary likelihood function, conduct a likelihood ratio hypothesis test (LRT) over each rectangular region in the grid, rank all of the rectangles based on the computed LRT statistics, and return the top few most interesting rectangles. To speed this process, we develop methods to prune rectangles without computing their associated LRT statistics.\n          <\/jats:p>","DOI":"10.1145\/1857947.1857952","type":"journal-article","created":{"date-parts":[[2010,11,1]],"date-time":"2010-11-01T13:32:33Z","timestamp":1288618353000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A Model-Agnostic Framework for Fast Spatial Anomaly Detection"],"prefix":"10.1145","volume":"4","author":[{"given":"Mingxi","family":"Wu","sequence":"first","affiliation":[{"name":"Oracle Corporation"}]},{"given":"Chris","family":"Jermaine","sequence":"additional","affiliation":[{"name":"Rice University"}]},{"given":"Sanjay","family":"Ranka","sequence":"additional","affiliation":[{"name":"University of Florida"}]},{"given":"Xiuyao","family":"Song","sequence":"additional","affiliation":[{"name":"Yahoo! Inc"}]},{"given":"John","family":"Gums","sequence":"additional","affiliation":[{"name":"University of Florida"}]}],"member":"320","published-online":{"date-parts":[[2010,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150410"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/1109557.1109683"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000000089"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000001211"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1056397487"},{"volume-title":"Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. AAAI Press, 226--231","author":"Ester M.","key":"e_1_2_1_7_1"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610929708831995"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Kulldorff M. 1999. Spatial scan statistics: Model calculations and applications. In Scan Statistics and Applications J. Glaz and M. Balakrishnan Eds. Birkhauses 303--322. Kulldorff M. 1999. Spatial scan statistics: Model calculations and applications. In Scan Statistics and Applications J. Glaz and M. Balakrishnan Eds. 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