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. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Zhao, Y., Zhang, H., Cao, L., Zhang, C., & Bohlscheid, H. (2009). Mining both positive and negative impact-oriented sequential rules from transactional data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5476 LNAI, pp. 656–663). https://doi.org/10.1007/978-3-642-01307-2_65
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