Abstract
Traditional sequential pattern mining deals with positive correlation between sequential patterns only, without considering negative relationship between them. In this paper, we present a notion of impact-oriented negative sequential rules, in which the left side is a positive sequential pattern or its negation, and the right side is a predefined outcome or its negation. Impact-oriented negative sequential rules are formally defined to show the impact of sequential patterns on the outcome, and an efficient algorithm is designed to discover both positive and negative impact-oriented sequential rules. Experimental results on both synthetic data and real-life data show the efficiency and effectiveness of the proposed technique.
This work was supported by the Australian Research Council (ARC) Linkage Project LP0775041 and Discovery Projects DP0667060 & DP0773412, and by the Early Career Researcher Grant from University of Technology, Sydney, Australia.
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Zhao, Y., Zhang, H., Cao, L., Zhang, C., Bohlscheid, H. (2009). Mining Both Positive and Negative Impact-Oriented Sequential Rules from Transactional Data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_65
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DOI: https://doi.org/10.1007/978-3-642-01307-2_65
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