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Testing terrorism theory with data mining

Author

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  • Anthony Scime
  • Gregg R. Murray
  • Lance Y. Hunter

Abstract

This research demonstrates the application of multiple data mining techniques to test theories of the macro-level causes of terrorism. The unique dataset is comprised of terrorist events and measures of social, political and economic contexts in 185 countries worldwide between the years 1970 and 2004. The theories are assessed using the iterative expert data mining (IEDM) methodology with classification mining and then association mining. The resulting 100 rules suggest that the level of democracy in a country is an integral part of the explanation for terrorism. This research shows that a multi-method data mining approach can be used to test competing theories in a discipline by analysing large, comprehensive datasets that capture multiple theories and include large numbers of records.

Suggested Citation

  • Anthony Scime & Gregg R. Murray & Lance Y. Hunter, 2010. "Testing terrorism theory with data mining," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 2(2), pages 122-139.
  • Handle: RePEc:ids:injdan:v:2:y:2010:i:2:p:122-139
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    Citations

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    Cited by:

    1. Amirali Saeedi & Toni L. Doolen, 2012. "A computer-assisted qualitative data analysis framework for the engineering management domain," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 1-20.
    2. Steven J. Jurek & Anthony Scime, 2014. "Achieving Democratic Leadership: A Data-Mined Prescription," Social Science Quarterly, Southwestern Social Science Association, vol. 95(1), pages 97-110, March.

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