dc.contributor.author | Lee, Ickjai | |
dc.contributor.author | Estivill-Castro, Vladimir | |
dc.date.accessioned | 2017-05-03T14:16:06Z | |
dc.date.available | 2017-05-03T14:16:06Z | |
dc.date.issued | 2011 | |
dc.date.modified | 2012-02-13T05:04:38Z | |
dc.identifier.issn | 0883-9514 | |
dc.identifier.doi | 10.1080/08839514.2011.570153 | |
dc.identifier.uri | http://hdl.handle.net/10072/42460 | |
dc.description.abstract | We incorporate two data mining techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns in data-rich environments. This tool is an autonomous pattern detector that efficiently and effectively reveals plausible cause-effect associations among many geographical layers. We present two methods for exploratory analysis and detail algorithms to explore massive databases. We illustrate the algorithms with real crime data sets. | |
dc.description.peerreviewed | Yes | |
dc.description.publicationstatus | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Taylor & Francis Inc. | |
dc.publisher.place | United States | |
dc.relation.ispartofstudentpublication | N | |
dc.relation.ispartofpagefrom | 362 | |
dc.relation.ispartofpageto | 379 | |
dc.relation.ispartofissue | 5 | |
dc.relation.ispartofjournal | Applied Artificial Intelligence | |
dc.relation.ispartofvolume | 25 | |
dc.rights.retention | Y | |
dc.subject.fieldofresearch | Cognitive and computational psychology | |
dc.subject.fieldofresearchcode | 5204 | |
dc.title | Exploration of Massive Crime Data Sets through Data Mining Techniques | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
gro.date.issued | 2011 | |
gro.hasfulltext | No Full Text | |
gro.griffith.author | Estivill-Castro, Vladimir | |