An overview of data warehousing and OLAP technology
ACM SIGMOD Record
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
COPLINK: managing law enforcement data and knowledge
Communications of the ACM
Data association methods with applications to law enforcement
Decision Support Systems
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Prediction of past unsolved terrorist attacks
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
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Serial criminals are a major threat in the modern society. Associating incidents committed by the same offender is of great importance in studying serial criminals. In this paper, we present a new outlier-based approach to resolve this criminal incident association problem. In this approach, criminal incident data are first modeled into a number of cells, and then a measurement function, called outlier score function, is defined over these cells. Incidents in a cell are determined to be associated with each other when the score is significant enough. We applied our approach to a robbery dataset from Richmond, VA. Results show that this method can effectively solve the criminal incident association problem.