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

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.