Mining Both Positive and Negative Impact-Oriented Sequential Rules from Transactional Data

Authors:
Yanchang Zhao;Huaifeng Zhang;Longbing Cao;Chengqi Zhang;Hans Bohlscheid
Affiliations:
Data Sciences and Knowledge Discovery Lab Faculty of Engineering & IT, University of Technology, Sydney, Australia;Data Sciences and Knowledge Discovery Lab Faculty of Engineering & IT, University of Technology, Sydney, Australia;Data Sciences and Knowledge Discovery Lab Faculty of Engineering & IT, University of Technology, Sydney, Australia;Data Sciences and Knowledge Discovery Lab Faculty of Engineering & IT, University of Technology, Sydney, Australia;Data Sciences and Knowledge Discovery Lab Faculty of Engineering & IT, University of Technology, Sydney, Australia and Projects Section, Business Integrity Programs Branch, Centrelink, Australia
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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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.