Terrorist Activity Evaluation and Pattern Detection (TAE&PD) in Afghanistan:A Knowledge Discovery and Data Mining (KDDM) Approach for Counter-Terrorism
Abstract
Data mining is primarily used by businesses. Today companies with strong consumer focus depend on data mining to determine relationships among internal and external data factors that are being registered and stored digitally. It enables them to drill down data into summary information to view detail transactional data and reveal trends that could be beneficial for an organization s business decision making. In this paper we incorporate data mining techniques using open source applications to model, evaluate and identify patterns of terrorism activity in Afghanistan for counter-terrorism and to strengthen homeland security. We apply data mining techniques to real terrorism incidents data from the Worldwide Incidents Tracking System (WITS) of the National Counterterrorism Center (NCTC). The results of the study will help in the discovery of terrorist group tendencies based on specific incident factors, but will also help evaluate the war on terror in Afghanistan up to date. With these results we also look to uncover valuable information regarding terrorist hot spots to determine geographical mobilization of security forces resources in the region.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2012
- Accession Number
- ADA581563
Entities
People
- Alfredo Cruz
- Jeff Duffany
- Jose Pou
Organizations
- Polytechnic University of Puerto Rico