An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Data association methods with applications to law enforcement
Decision Support Systems
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Proceedings of the 27th International Conference on Very Large Data Bases
Cluster Analysis
Automated criminal link analysis based on domain knowledge: Research Articles
Journal of the American Society for Information Science and Technology
Where do I start?: algorithmic strategies to guide intelligence analysts
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
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Associating criminal incidents committed by the same person is important in crime analysis. In this paper, we introduce concepts from OLAP (online-analytical processing) and data-mining to resolve this issue. The criminal incidents are modeled into an OLAP data cube; a measurement function, called the outlier score function is defined on the cube cells. When the score is significant enough, we say that the incidents contained in the cell are associated with each other. The method can be used with a variety of criminal incident features to include the locations of the crimes for spatial analysis. We applied this association method to the robbery dataset of Richmond, Virginia. Results show that this method can effectively solve the problem of criminal incident association.