Data Mining: Study on Intelligence-Led Counterterrorism

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 144)


This paper establishes a relatively complete theoretical framework, with multi-disciplinary perspective. Intelligence-led counterterrorism framework based on data mining can be divided into four function modules: data storage, data pretreatment, data analysis and data visualization processor. The data mining process of counterterrorism’s information analysis includes problem identification of counterterrorism, data preparation, data mining, model assessment and knowledge representation. Bayesian Belief Network is a very useful tool for data mining in intelligence-led counterterrorism.

Terrorists more or less will leave the corresponding clues in premeditated, planning and implementing crime. However, it is harder than looking for a needle in a bottle of hay to use the existing technology and method to extract the antiterrorist information from the clues! Because this kind of terrorism information which is covered in the magnitude of the general information is indefinite and unknown in time series and space. The traditional database retrieval system cannot respond to this inquiry. Artificial identification, mutatis mutandis, monitoring technology and the implementation of known goal detection all need lots of manpower, and it is powerless to search unknown target in voluminous information. But the Data Mining can do!


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of Defense Economics and ManagementCentral University of Finance and EconomicsBeijingP.R. China

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