Title :
Prediction of past unsolved terrorist attacks
Author :
Ozgul, Fatih ; Erdem, Zeki ; Bowerman, Chris
Author_Institution :
Dept. of Comput. &Technol., Univ. of Sunderland, Sunderland
Abstract :
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 - ldquoumbrellardquo 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.
Keywords :
data mining; pattern classification; pattern clustering; police data processing; terrorism; unsupervised learning; CPM; crime prediction model; data mining; past unsolved terrorist attack prediction; pattern classification; pattern clustering; unsupervised learning; Cities and towns; Computers; Data mining; Demography; Event detection; Informatics; Information analysis; Intelligent networks; Predictive models; Terrorism; Terrorist (offender) groups; classification; clustering; crime data mining; group detection; matching and predicting crimes;
Conference_Titel :
Intelligence and Security Informatics, 2009. ISI '09. IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4171-6
Electronic_ISBN :
978-1-4244-4173-0
DOI :
10.1109/ISI.2009.5137268
