Mining for offender group detection and story of a police operation

  • Authors:
  • Fatih Ozgul;Julian Bondy;Hakan Aksoy

  • Affiliations:
  • University of Sunderland, UK;RMIT University, Melbourne, Australia;Information Processing Unit, Bursa, Turkey

  • Venue:
  • AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

Since discovery of an underlying organisational structure from crime data leads the investigation to terrorist cells or organised crime groups, detecting covert networks are important to crime investigation. As shown in application of Offender Group Detection Model (OGDM), which is developed and tested on a theft network in Bursa, Turkey, use of effective data mining methods can reveal offender groups. OGDM detected seven ruling members of twenty network members. Based on initial findings of OGDM; thirty-four offenders are considered to be in a single offender group where seven of them were ruling members. After Operation Cash was launched, the police arrested the seven detected ruling members, and confirmed that the real crime network was consisting of 20 members of which 3 whom had never been previously identified or arrested. The police arrested 17 people, recovered worth U.S. $ 200,000 of stolen goods, and cash worth U.S. $ 180,000.