Testing terrorism theory with data mining

Anthony Scime, Gregg R. Murray and Lance Y. Hunter

International Journal of Data Analysis Techniques and Strategies, 2010, vol. 2, issue 2, 122-139

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.

Keywords: association mining; classification; data dimensionality reduction; iterative expert data mining; decision trees; IEDM; rule reduction; significance testing; social science theory; terrorism causes; data analysis; testing theory. (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:2:y:2010:i:2:p:122-139

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