Skip to main content

Mining Sequential Patterns in Large Datasets

  • Conference paper
Fuzzy Systems and Knowledge Discovery (FSKD 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4223))

Included in the following conference series:

  • 1251 Accesses

Abstract

A novel algorithm FFSPAN (Fast Frequent Sequential Pattern mining algorithm) is proposed in this paper. FFSPAN mines all the frequent sequential patterns in large datasets, and solves the problem of searching frequent sequences in a sequence database by searching frequent items or frequent itemsets. Moreover, the databases that FFSPAN scans keep shrinking quickly, which makes the algorithm more efficient when the sequential patterns are longer. Experiments on standard test data show that FFSPAN is very effective.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: ICDE 1995, Taipei, Taiwan (1995)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, Santiago, Chile, pp. 487–499 (1994)

    Google Scholar 

  3. Burdick, D., Calimlim, M., Gehrke, J.: Mafia: A maximal frequent itemset algorithm for transactional databases. In: ICDE 2001, Heidelberg, Germany (2001)

    Google Scholar 

  4. Yan, X., Han, J., Afshar, R.: CloSpan: Mining Closed Sequential Patterns in Large Databases. In: SDM 2003, San Francisco, California, USA (2003)

    Google Scholar 

  5. Ayres, J., Flannick, J., Gehrke, J., et al.: Sequential pattern mining using a bitmap representation. In: SIGKDD 2002, Edmonton, Alberta, Canada, pp. 429–435 (2002)

    Google Scholar 

  6. 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), April 2001, pp. 215–224. Heidelberg, Germany (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chang, XY., Zhou, CG., Wang, Z., Li, YW., Hu, P. (2006). Mining Sequential Patterns in Large Datasets. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_84

Download citation

  • DOI: https://doi.org/10.1007/11881599_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45916-3

  • Online ISBN: 978-3-540-45917-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics