Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining e-mail content for author identification forensics
ACM SIGMOD Record
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Automatically detecting deceptive criminal identities
Communications of the ACM - Homeland security
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
URBAN CRIME ANALYSIS THROUGH AREAL CATEGORIZED MULTIVARIATE ASSOCIATIONS MINING
Applied Artificial Intelligence
An ontology-based intrusion alerts correlation system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Mining qualitative patterns in spatial cluster analysis
Expert Systems with Applications: An International Journal
Topic correlation and individual influence analysis in online forums
Expert Systems with Applications: An International Journal
Geographic knowledge discovery from Web Map segmentation through generalized Voronoi diagrams
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Crime activities are geospatial phenomena and as such are geospatially, thematically and temporally correlated. We analyze crime datasets in conjunction with socio-economic and socio-demographic factors to discover co-distribution patterns that may contribute to the formulation of crime. We propose a graph based dataset representation that allows us to extract patterns from heterogeneous areal aggregated datasets and visualize the resulting patterns efficiently. We demonstrate our approach with real crime datasets and provide a comparison with other techniques.