Mining the acceleration-like association rules

Authors:
Dechang Pi;Xiaolin Qin;Wangfeng Gu
Affiliations:
College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, P.R. China;College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, P.R. China;College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, P.R. China
Venue:
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Year:
2006

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Abstract

A new rule called Acceleration-like Association Rule that denotes the evolving direction of rules is proposed by analyzing the changes of the support and confidence in the primitive dataset and the increment dataset. Although this kind of rules can’t be strong in the final rule set, they can decide the developing trend of the rule. Experiment with the UCI datasets shows that our algorithm can efficiently discover this kind of rules which are very useful for decision, including criminal symptom analysis and terrorism forecasting.