Multi-dimensional Sequential Pattern Mining Based on Concept Lattice

  • Yang Jin
  • Wanli Zuo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Multi-dimensional sequential pattern mining attempts to find much more informative frequent patterns suitable for immediate use. In this paper, a novel data model called multi-dimensional concept lattice is proposed and, based on which, a new incremental multi-dimensional sequential pattern mining algorithm is developed. The proposed algorithm integrates sequential pattern mining and association pattern mining with a uniform data structure and makes the mining process more efficient. The performance of the proposed approach is evaluated on both synthetic and real-life financial date sets.


Sequential Pattern Pattern Mining Synthetic Dataset Association Rule Mining Concept Lattice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. 1995 Int.Conf. Data Engineering (ICDE 1995), Taipei, Taiwan, March 1995, pp. 3–14 (1995)Google Scholar
  2. 2.
    Pinto, H., Han, J., Pei, J., Wang, K., Chen, Q., Dayal, U.: Multi-Dimensional Sequential Pattern Mining. In: CIKM 2001, pp. 81–88 (2001)Google Scholar
  3. 3.
    Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In: Proc. 2001 Int. Conf. Data Engineering (ICDE 2001), Heidelberg, Germany, April 2001, pp. 215–224 (2001)Google Scholar
  4. 4.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. 2000 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD 2000), Dallas, TX, May 2000, pp. 1–12 (2000)Google Scholar
  5. 5.
    Wille, R.: Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts. In: Rival, I. (ed.) Ordered Set, pp. 445–470 (1982)Google Scholar
  6. 6.
    Jin, Y., Zuo, W.: Ordered Concept Lattice and WWW User Transversal Pattern Mining (in Chinese). Journal of Computer Research and Development 40(5), 675–683 (2003)Google Scholar
  7. 7.
    Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)Google Scholar
  8. 8.
    van der Merwe, D., Obiedkov, S.A., Kourie, D.G.: AddIntent: A New Incremental Algorithm for Constructing Concept Lattices. In: Eklund, P.W. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 372–385. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yang Jin
    • 1
  • Wanli Zuo
    • 1
  1. 1.College of Computer Science & TechnologyJiLin UniversityChangChunP.R. China

Personalised recommendations