C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Using conjunction of attribute values for classification
Proceedings of the eleventh international conference on Information and knowledge management
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The involvement of human resources in large scale data mining projects
ISICT '03 Proceedings of the 1st international symposium on Information and communication technologies
Countering terrorism through information technology
Communications of the ACM - Homeland security
Evidence Combination in Medical Data Mining
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Expert Systems: Principles and Programming
Expert Systems: Principles and Programming
Information Technology for Management: Transforming Organizations in the Digital Economy
Information Technology for Management: Transforming Organizations in the Digital Economy
Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Hybrid Learning Scheme for Data Mining Applications
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
US Domestic Extremist Groups on the Web: Link and Content Analysis
IEEE Intelligent Systems
Data Mining to Combat Terrorism and the Roots of Privacy Concerns
Ethics and Information Technology
Citizen centric analysis of anti/counter-terrorism e-government services
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Harvesting Terrorists Information from Web
IV '07 Proceedings of the 11th International Conference Information Visualization
Evaluating the efficacy of a terrorism question/answer system
Communications of the ACM - Creating a science of games
Privacy-preserving distributed association rule mining via semi-trusted mixer
Data & Knowledge Engineering
Leveraging Question Answer technology to address terrorism inquiry
Decision Support Systems
A framework for agent-based distributed machine learning and data mining
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A Hybrid Classification Scheme for Mining Multisource Geospatial Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
International Journal of Business Intelligence and Data Mining
Vote prediction by iterative domain knowledge and attribute elimination
International Journal of Business Intelligence and Data Mining
Investigative data mining and its application in counterterrorism
AIC'05 Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications
Predicting credit card customer churn in banks using data mining
International Journal of Data Analysis Techniques and Strategies
A Study of Classification Algorithm for Data Mining Based on Hybrid Intelligent Systems
SNPD '08 Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Segmenting the mature travel market by motivation
International Journal of Data Analysis Techniques and Strategies
Terrorism Informatics: Knowledge Management and Data Mining for Homeland Security
Terrorism Informatics: Knowledge Management and Data Mining for Homeland Security
Finding "persistent rules": Combining association and classification results
Expert Systems with Applications: An International Journal
Simulation and Gaming
A goal model-driven supply chain design
International Journal of Data Analysis Techniques and Strategies
Deriving strong association mining rules using a dependency criterion, the lift measure
International Journal of Data Analysis Techniques and Strategies
International Journal of Data Analysis Techniques and Strategies
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
IEEE Transactions on Knowledge and Data Engineering
An investigation of TREPAN utilising a continuous oracle model
International Journal of Data Analysis Techniques and Strategies
A computer-assisted qualitative data analysis framework for the engineering management domain
International Journal of Data Analysis Techniques and Strategies
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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.