Prediction of Unsolved Terrorist Attacks Using Group Detection Algorithms

  • Fatih Ozgul
  • Zeki Erdem
  • Chris Bowerman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5477)


Detection of terrorist groups using crime data has few examples currently; this is because of lack of detailed crime data which contain terrorist groups’ attacks and activities. In this study, a novel prediction model; CPM is applied to a crime dataset which includes solved and unsolved terrorist events in Istanbul, Turkey between 2003 and 2005, aiming to predict perpetuators of terrorist events which are still remained unsolved. CPM initially learns similarities of crime incident attributes from all terrorist attacks and then puts them in appropriate clusters. After solved and unsolved attacks are gathered in the same “umbrella” clusters, CPM classifies unsolved cases to previously known terrorist groups. Recall and precision results and findings of CPM regarded as successful then a baseline system; TMODS.


Terrorist groups crime data mining matching and predicting crimes clustering classification offender networks group detection 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fatih Ozgul
    • 1
  • Zeki Erdem
    • 2
  • Chris Bowerman
    • 1
  1. 1.Department of Computing &TechnologyUniversity of SunderlandSunderlandUnited Kingdom
  2. 2.Information Technologies InstituteTUBITAK- Marmara Research CentreGebzeTurkey

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