Skip to main content
SpringerLink
Log in

A fast algorithm for mining sequential patterns from large databases

  • Correspondence
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Mining sequential patterns from large databases has been recognized by many researchers as an attractive task of data mining and knowledge discovery. Previous algorithms scan the databases for many times, which is often unendurable due to the, very large amount of databases. In this paper, the authors introduce an effective algorithm for mining sequential patterns from large databases. In the algorithm, the original database is not used at all for counting the support of sequences after the first pass. Rather, a tidlist structure generated in the previous pass is employed for the purpose based on set intersection operations, avoiding the multiple scans of the databases.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen Ming-Syan, Han Jiawei, Yu Philip S. Data mining: An overview from a database perspective.IEEE Transactions on Knowledge and Data Engineering, Dec., 1996, 8(6): 866–883.

    Article  Google Scholar 

  2. Rakesh Agrawal, Tomasz Imielinski, Arun Swarni Mining association rules between sets of items in large databases. InProceedings of ACM SIGMOD, May, 1993, pp. 207–216.

  3. Chen Ning, Zhon Longxiang. Data mining in Internet.Computer Science, 1999, 26(7): 44–49. (in Chinese).

    Google Scholar 

  4. Rakesh Agrawal, Ramakrishnan Srikant. Mining sequential patterns. InProceedings of Data Engineering Conference, Taipei, March, 1995, pp. 3–14.

  5. Houtsma M, Swami A. Set-oriented mining of association rules. Research Report RJ 9567, IBM Almaden Research Center, San Jose, California, October, 1993.

    Google Scholar 

  6. Rakesh Agrawal Ramakrishnan Srikant, Fast algorithm for mining association rules, InProceedings of the 20th VLDB Conference, Santiago, Chile, 1994, pp. 487–499.

  7. Park J-S, Chen M-S, Yu P S. An effective Hash based algorithm for mining association rules. InProceedings of ACM SIGMOD, May, 1995, pp. 175–186.

  8. Savasere A, Omiecinski E, Navathe S. An efficient algorithm for mining association rules in large databases. InProceedings of International Conference of VLDB, 1995, pp. 432–444.

  9. Mohammed Javeed Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, Wei Li. Parallel algorithms for discovery of association rules.Data Mining and Knowledge Discovery, 1997, 1(4): 343–373.

    Article  Google Scholar 

  10. Chen Ning, Chen An. Discovery of multi-level sequential patterns from large database. InProceedings of the International Symposium on Future Software Technology, Software Engineers Association, Nanjing, China October 24–30, 1999, pp. 169–174.

Download references

Author information

Authors and Affiliations

  1. Academy of Mathematics and System Science, The Chinese Academy of Sciences, 100080, Beijing, P.R. China

    Chen Ning & Zhou Longxiang

  2. Institute of Policy and Management, The Chinese Academy of Sciences, 100080, Beijing, P.R. China

    Chen An & Liu Lu

Authors
  1. Chen Ning
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen An
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhou Longxiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liu Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chen Ning.

Additional information

CHEN Ning was born in 1974. She received her B.S. and M.S. degrees from Department of Computer Science of Shandong University in 1994 and 1997, respectively. She received the Ph.D. degree from Academy of Mathematics and System Science, The Chinese Academy of Sciences in 2001. Her current research interests include data mining and knowledge discovery.

CHEN An was born in 1970. He received his M.S. degree from School of Mathematics and System Science of Shandong University in 1997. He received the Ph.D. degree from Beijng University of Aeronautics and Astronautics in 2001. He is now an assistant professor in the Institute of Plicy and Management, The Chinese Academy of Sciences. His current research interests include supply chain management and E-commerce.

ZHOU Longxiang was born in 1938. He is now a professor of Academy of Mathematics and System Science, The Chinese Academy of Sciences. His research interests include distributed database, data mining, E-commerce, and data warehouse.

LIU Lu was born in 1947. She received her M.S. degree from Beijing Polytechnic University in 1988. She is a professor of Beijing University of Aeronautics and Astronautics. Her research interests include MIS, supply chain and E-commerce.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, N., Chen, A., Zhou, L. et al. A fast algorithm for mining sequential patterns from large databases. J. Comput. Sci. & Technol. 16, 359–370 (2001). https://doi.org/10.1007/BF02948984

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02948984

Keywords

Search

Navigation