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.
This work was supported by the Australian Research Council (ARC) Linkage Project LP0775041 and Discovery Projects DP0667060 & DP0773412, and Early Career Researcher Grant RM2007002447 from University of Technology, Sydney, Australia.
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Zhao, Y., Zhang, H., Cao, L., Zhang, C., Bohlscheid, H. (2008). Combined Pattern Mining: From Learned Rules to Actionable Knowledge. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_40
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DOI: https://doi.org/10.1007/978-3-540-89378-3_40
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