Data mining case study: modeling the behavior of offenders who commit serious sexual assaults
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Graph-based technologies for intelligence analysis
Communications of the ACM - Homeland security
Mining Graph Data
Using Social Contextual Information to Match Criminal Identities
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 04
An outlier-based data association method for linking criminal incidents
Decision Support Systems - Special issue: Intelligence and security informatics
Classification system for serial criminal patterns
Artificial Intelligence and Law
POLESTAR: collaborative knowledge management and sensemaking tools for intelligence analysts
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Crime Pattern Detection Using Data Mining
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Intelligent computer evaluation of offender's previous record
Artificial Intelligence and Law
Predicting criminal relationships using multivariate survival analysis
dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
Mining for offender group detection and story of a police operation
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
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In this study, a novel model is proposed to predict perpetuators of some terrorist events which are remain unsolved. The CPM learns from similarities between terrorist attacks and their crime attributes then puts them in appropriate clusters. Solved and unsolved attacks are gathered in the same - all linked to each other - "umbrella" clusters; then CPM classifies all related terrorist events which are expected to belong to one single terrorist group. The developed model is applied to a real crime dataset, which includes solved and unsolved terrorist attacks and crimes in Turkey between 1970 and 2005. CPM predictions produced significant precision value for big terrorist groups and reasonable recall values for small terrorist groups.