Mining top-k and Bottom-k correlative crime patternsthrough graph representations

Authors:
Peter Phillips;Ickjai Lee
Affiliations:
School of Business, James Cook University, Townsville, QLD, Australia;School of Business, James Cook University, Cairns, QLD, Australia
Venue:
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Year:
2009

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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.