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)


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.


negative sequential rules sequential pattern mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD Int. Conf. on Management of Data, Washington D.C., USA, May 1993, pp. 207–216 (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Yu, P.S., Chen, A.S.P. (eds.) Proc. of the 11th Int. Conf. on Data Engineering, Taipei, Taiwan, pp. 3–14 (1995)Google Scholar
  3. 3.
    Antonie, M.-L., Zaïane, O.R.: Mining positive and negative association rules: an approach for confined rules. In: Proc. of the 8th Eur. Conf. on Principles and Practice of Knowledge Discovery in Databases, New York, USA, pp. 27–38 (2004)Google Scholar
  4. 4.
    Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: KDD 2002: Proc. of the 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 429–435 (2002)Google Scholar
  5. 5.
    Bannai, H., Hyyro, H., Shinohara, A., Takeda, M., Nakai, K., Miyano, S.: Finding optimal pairs of patterns. In: Jonassen, I., Kim, J. (eds.) WABI 2004. LNCS (LNBI), vol. 3240, pp. 450–462. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Han, J., Pei, J., et al.: Freespan: frequent pattern-projected sequential pattern mining. In: KDD 2000: Proc. of the 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 355–359 (2000)Google Scholar
  7. 7.
    Lin, N.P., Chen, H.-J., Hao, W.-H.: Mining negative sequential patterns. In: Proc. of the 6th WSEAS Int. Conf. on Applied Computer Science, Hangzhou, China, pp. 654–658 (2007)Google Scholar
  8. 8.
    Ouyang, W.-M., Huang, Q.-H.: Mining negative sequential patterns in transaction databases. In: Proc. of 2007 Int. Conf. on Machine Learning and Cybernetics, Hong Kong, China, pp. 830–834 (2007)Google Scholar
  9. 9.
    Pei, J., Han, J., et al.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE 2001: Proc. of the 17th Int. Conf. on Data Engineering, Washington, DC, USA, p. 215 (2001)Google Scholar
  10. 10.
    Savasere, A., Omiecinski, E., Navathe, S.B.: Mining for strong negative associations in a large database of customer transactions. In: ICDE 1998: Proc. of the 14th Int. Conf. on Data Engineering, Washington, DC, USA, pp. 494–502 (1998)Google Scholar
  11. 11.
    Sun, X., Orlowska, M.E., Li, X.: Finding negative event-oriented patterns in long temporal sequences. In: Proc. of the 8th Pacific-Asia Conf. on Knowledge Discovery and Data Mining, Sydney, Australia, pp. 212–221 (May 2004)Google Scholar
  12. 12.
    Wu, X., Zhang, C., Zhang, S.: Efficient mining of both positive and negative association rules. ACM Transactions on Information Systems 22(3), 381–405 (2004)CrossRefGoogle Scholar
  13. 13.
    Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Machine Learning 42(1-2), 31–60 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Zhao, Y., Zhang, H., Cao, L., Zhang, C., Bohlscheid, H.: Efficient mining of event-oriented negative sequential rules. In: Proc. of the 2008 IEEE/WIC/ACM Int. Conf. on Web Intelligence (WI 2008), Sydney, Australia, pp. 336–342 (December 2008)Google Scholar

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

Personalised recommendations