{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,5,7]],"date-time":"2022-05-07T02:15:50Z","timestamp":1651889750120},"reference-count":26,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1,1]]},"abstract":"<p>Anomaly region detection aims at finding spatial outliers or spatial anomalous clusters. Generally, detection approaches cover spatial neighbor's discovery with spatial attributes and anomaly measurement of spatial regions according to non-spatial attributes. In this article, an anomaly region detection method using Delaunay minimal spanning tree (DMST for short) is proposed. First, a Delaunay minimal spanning tree is constructed. Then, the current longest edge of the tree is iteratively cut and anomaly regions are concurrently detected. Finally, the shortest edge of the related bipartite graph is taken as the anomaly measurement. The proposed method could avoid the disturbance of bad reference neighbors and generate anomaly regions keeping atomicity.<\/p>","DOI":"10.4018\/ijdwm.2019010103","type":"journal-article","created":{"date-parts":[[2019,2,6]],"date-time":"2019-02-06T13:54:13Z","timestamp":1549461253000},"page":"39-57","source":"Crossref","is-referenced-by-count":0,"title":["Anomaly Region Detection Based on DMST"],"prefix":"10.4018","volume":"15","author":[{"given":"Sulan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Big Data and Intelligent Engineering, Yangtze Normal University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqiang","family":"Wan","sequence":"additional","affiliation":[{"name":"Country Garden, Foshan City, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2432","reference":[{"key":"IJDWM.2019010103-0","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0365-y"},{"key":"IJDWM.2019010103-1","doi-asserted-by":"publisher","DOI":"10.1145\/116873.116880"},{"key":"IJDWM.2019010103-2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-005-0200-2"},{"key":"IJDWM.2019010103-3","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2015.09.037"},{"key":"IJDWM.2019010103-4","author":"J.Han","year":"2011","journal-title":"Data mining: concepts and techniques"},{"key":"IJDWM.2019010103-5","author":"D.Hawkins","year":"2013","journal-title":"Identification of Outliers"},{"key":"IJDWM.2019010103-6","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.71"},{"key":"IJDWM.2019010103-7","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.96"},{"key":"IJDWM.2019010103-8","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2009-0357"},{"key":"IJDWM.2019010103-9","doi-asserted-by":"publisher","DOI":"10.14778\/3007263.3007308"},{"key":"IJDWM.2019010103-10","doi-asserted-by":"crossref","unstructured":"Kou, Y., Lu, C. T., & Chen, D. (2006). Spatial weighted outlier detection. Proceedings in Applied Mathematics 124, Bethesda.","DOI":"10.1137\/1.9781611972764.71"},{"key":"IJDWM.2019010103-11","article-title":"Spatial outlier detection: a graph-based approach.","author":"Y.Kou","year":"2007","journal-title":"19th IEEE International Conference on Tools with Artificial Intelligence"},{"key":"IJDWM.2019010103-12","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2013.2239959"},{"key":"IJDWM.2019010103-13","doi-asserted-by":"publisher","DOI":"10.1145\/1869790.1869841"},{"key":"IJDWM.2019010103-14","article-title":"Outlier detection using random walks.","author":"H. D. K.Moonesinghe","year":"2006","journal-title":"18th IEEE International Conference on Tools with Artificial Intelligence"},{"key":"IJDWM.2019010103-15","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2675710"},{"key":"IJDWM.2019010103-16","doi-asserted-by":"publisher","DOI":"10.1007\/s10940-016-9321-x"},{"key":"IJDWM.2019010103-17","unstructured":"Shekhar, S., & Chawla, S. (2003). Spatial databases: a tour. New Jersey: Upper Saddle River."},{"key":"IJDWM.2019010103-18","doi-asserted-by":"crossref","unstructured":"Shekhar, S., Lu, C. T., & Zhang, P. (2001). Detecting graph-based spatial outliers: algorithms and applications. In Proceedings of the seventh ACM SIGKDD,International Conference on Knowledge Discovery and Data Mining, San Francisco, CA.","DOI":"10.1145\/502512.502567"},{"key":"IJDWM.2019010103-19","doi-asserted-by":"publisher","DOI":"10.1023\/A:1023455925009"},{"key":"IJDWM.2019010103-20","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-7721(01)00047-5"},{"key":"IJDWM.2019010103-21","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.212"},{"key":"IJDWM.2019010103-22","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12208"},{"key":"IJDWM.2019010103-23","doi-asserted-by":"publisher","DOI":"10.1186\/1476-072X-4-11"},{"key":"IJDWM.2019010103-24","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0366-x"},{"key":"IJDWM.2019010103-25","doi-asserted-by":"publisher","DOI":"10.1109\/CIS.2008.121"}],"container-title":["International Journal of Data Warehousing and Mining"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=223136","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,7]],"date-time":"2022-05-07T01:42:27Z","timestamp":1651887747000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJDWM.2019010103"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2019,1,1]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1]]}},"URL":"https:\/\/doi.org\/10.4018\/ijdwm.2019010103","relation":{},"ISSN":["1548-3924","1548-3932"],"issn-type":[{"value":"1548-3924","type":"print"},{"value":"1548-3932","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,1]]}}}