Advertisement

IMCS: Incremental Mining of Closed Sequential Patterns

  • Lei Chang
  • Dongqing Yang
  • Tengjiao Wang
  • Shiwei Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4505)

Abstract

Recently, mining compact frequent patterns (for example closed patterns and compressed patterns) has received much attention from data mining researchers. These studies try to address the interpretability and efficiency problems encountered by traditional frequent pattern mining methods. However, to the best of our knowledge, how to efficiently mine compact sequential patterns in a dynamic sequence database environment has not been explored yet.

In this paper, we examine the problem how to mine closed sequential patterns incrementally. A compact structure CSTree is designed to keep the closed sequential patterns, and an efficient algorithm IMCS is developed to maintain the CSTree when the sequence database is updated incrementally. A thorough experimental study shows that IMCS outperforms the state-of-the-art algorithms – PrefixSpan, CloSpan, BIDE and a recently proposed incremental mining algorithm IncSpan by about a factor of 4 to more than an order of magnitude.

Keywords

Sequential Pattern Frequent Pattern Hash Table Pattern Mining Sequential Pattern Mining 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential PAttern Mining using A Bitmap Representation. In: Int. Conf. on Knowledge Discovery and Data Mining (2002)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Int. Conf. on Data Engineering (1995)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Int. Conf. on Extending Database Technology (1996)Google Scholar
  4. 4.
    Cheng, H., Yan, X., Han, J.: IncSpan: Incremental Mining of Sequential Patterns in Large Database. In: Int. Conf. on Knowledge Discovery and Data Mining (2004)Google Scholar
  5. 5.
    Chang, L., Yang, D.-q., Tang, S.-w., Wang, T.: Mining Compressed Sequential Patterns. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 761–768. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Kao, B., Zhang, M., Yip, C., Cheung, D.W.: Efficient Algorithms for Mining and Incremental Update of Maximal Frequent Sequences. Data Mining and Knowledge Discovery (2005)Google Scholar
  7. 7.
    Lin, M., Lee, S.: Incremental Update on Sequential Patterns in Large Databases by Implicit Merging and Efficient Counting. Information System (2004)Google Scholar
  8. 8.
    Masseglia, F., Poncelet, P., Teisseire, M.: Incremental Mining of Sequential Patterns in Large Databases. Data & Knowledge Engineering (2003)Google Scholar
  9. 9.
    Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In: Int. Conf. on Data Engineering (2001)Google Scholar
  10. 10.
    Parthasarathy, S., Zaki, M.J., Ogihara, M., Dwarkadas, S.: Incremental and Interactive Sequence Mining. In: Int. Conf. on Information and Knowledge Management (1999)Google Scholar
  11. 11.
    Xin, D., Han, J., Yan, X., Cheng, H.: Mining Compressed Frequent-Pattern Sets. In: Int. Conf. on Very Large Data Bases (2005)Google Scholar
  12. 12.
    Yan, X., Han, J., Afshar, R.: CloSpan: Mining Closed Sequential Patterns in Large Datasets. In: SIAM Int. Conf. on Data Mining (2003)Google Scholar
  13. 13.
    Yan, X., Cheng, H., Han, J., Xin, D.: Summarizing Itemset Patterns: A Profile-Based Approach. In: Int. Conf. on Knowledge Discovery and Data Mining (2005)Google Scholar
  14. 14.
    Wang, J., Han, J.: BIDE: Efficient Mining of Frequent Closed Sequences. In: Int. Conf. on Data Engineering (2004)Google Scholar
  15. 15.
    Zaki, M.J.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning (2001)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Lei Chang
    • 1
  • Dongqing Yang
    • 1
  • Tengjiao Wang
    • 1
  • Shiwei Tang
    • 1
  1. 1.Department of Computer Science & Technology, Peking University, BeijingChina

Personalised recommendations