Mining top-k and bottom-k correlative crime patterns through graph representations

Phillips, Peter, and Lee, Ickjai (2009) Mining top-k and bottom-k correlative crime patterns through graph representations. In: Proceedings of the IEEE International Conference on Intelligence and Security Informatics 2009. pp. 25-30. From: IEEE International Conference on Intelligence and Security Informatics 2009, 8-11 June 2009, Dallas, Texas, USA.

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Abstract

Crime activities are geospatial phenomena and as such are geospatially, thematically and temporally correlated. Thus, crime datasets must be interpreted and analyzed in conjunction with various factors that can contribute to the formulation of crime. Discovering these correlations allows a deeper insight into the complex nature of criminal behavior. We introduce a graph based dataset representation that allows us to mine a set of datasets for correlation. We demonstrate our approach with real crime datasets and provide a comparison with other techniques.

Item ID: 7921
Item Type: Conference Item (Research - E1)
ISBN: 978-1-4244-4172-3
Keywords: correlation mining, crime data mining
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Date Deposited: 15 Jan 2010 01:09
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 50%
09 ENGINEERING > 0909 Geomatic Engineering > 090903 Geospatial Information Systems @ 50%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 100%
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