Testing terrorism theory with data mining

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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 languageEnglish (US)
Pages (from-to)122-139
Number of pages18
JournalInternational Journal of Data Analysis Techniques and Strategies
Issue number2
StatePublished - Jan 1 2010
Externally publishedYes


  • 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


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