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dc.contributor.authorLee, Ickjaien_US
dc.contributor.authorEstivill-Castro, Vladimiren_US
dc.date.accessioned2017-04-24T11:31:18Z
dc.date.available2017-04-24T11:31:18Z
dc.date.issued2011en_US
dc.date.modified2012-02-13T05:04:38Z
dc.identifier.issn08839514en_US
dc.identifier.doi10.1080/08839514.2011.570153en_US
dc.identifier.urihttp://hdl.handle.net/10072/42460
dc.description.abstractWe 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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.publisherTaylor & Francis Inc.en_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom362en_US
dc.relation.ispartofpageto379en_US
dc.relation.ispartofissue5en_US
dc.relation.ispartofjournalApplied Artificial Intelligenceen_US
dc.relation.ispartofvolume25en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080109en_US
dc.titleExploration of Massive Crime Data Sets through Data Mining Techniquesen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2011
gro.hasfulltextNo Full Text


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