Testing terrorism theory with data mining

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

doi:10.1504/IJDATS.2010.032453

Testing terrorism theory with data mining

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

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

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 multimethod 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.

Original language	English (US)
Pages (from-to)	122-139
Number of pages	18
Journal	International Journal of Data Analysis Techniques and Strategies
Volume	2
Issue number	2
DOIs	https://doi.org/10.1504/IJDATS.2010.032453
State	Published - 2010
Externally published	Yes

Keywords

IEDM
association mining
classification
data analysis
data dimensionality reduction
data mining
decision trees
rule reduction
significance testing
social science theory
terrorism
testing theory

ASJC Scopus subject areas

Information Systems
Information Systems and Management
Applied Mathematics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1504/IJDATS.2010.032453

Cite this

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AU - Hunter, Lance Y.

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AB - 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 multimethod 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.

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KW - data analysis

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KW - decision trees

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Testing terrorism theory with data mining

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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