Combined Pattern Mining: From Learned Rules to Actionable Knowledge

Authors:
Yanchang Zhao;Huaifeng Zhang;Longbing Cao;Chengqi Zhang;Hans Bohlscheid
Affiliations:
Data Sciences & Knowledge Discovery Research Lab Centre for Quantum Computation and Intelligent Systems Faculty of Engineering & IT, University of Technology, Sydney, Australia;Data Sciences & Knowledge Discovery Research Lab Centre for Quantum Computation and Intelligent Systems Faculty of Engineering & IT, University of Technology, Sydney, Australia;Data Sciences & Knowledge Discovery Research Lab Centre for Quantum Computation and Intelligent Systems Faculty of Engineering & IT, University of Technology, Sydney, Australia;Data Sciences & Knowledge Discovery Research Lab Centre for Quantum Computation and Intelligent Systems Faculty of Engineering & IT, University of Technology, Sydney, Australia;Projects Section, Business Integrity Programs Branch, Centrelink, Australia
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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Abstract

Association mining often produces large collections of association rules that are difficult to understand and put into action. In this paper, we have designed a novel notion of combined patterns to extract useful and actionable knowledge from a large amount of learned rules. We also present definitions of combined patterns, design novel metrics to measure their interestingness and analyze the redundancy in combined patterns. Experimental results on real-life social security data demonstrate the effectiveness and potential of the proposed approach in extracting actionable knowledge from complex data.