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

  • Yanchang Zhao
  • Huaifeng Zhang
  • Longbing Cao
  • Chengqi Zhang
  • Hans Bohlscheid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5476)

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.

Keywords

negative sequential rules sequential pattern mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yanchang Zhao
    • 1
  • Huaifeng Zhang
    • 1
  • Longbing Cao
    • 1
  • Chengqi Zhang
    • 1
  • Hans Bohlscheid
    • 1
    • 2
  1. 1.Data Sciences and Knowledge Discovery Lab Faculty of Engineering & ITUniversity of TechnologySydneyAustralia
  2. 2.Projects SectionBusiness Integrity Programs BranchCentrelinkAustralia

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