Post Sequential Patterns Mining

A New Method for Discovering Structural Patterns
  • Jing Lu
  • Osei Adjei
  • Weiru Chen
  • Jun Liu
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 163)


In this paper we present a novel data mining technique, known as Post Sequential Patterns Mining, which can be used to discover Structural Patterns. A Structural Pattern is a new pattern, which is composed of sequential patterns, branch patterns or iterative patterns. Sequential patterns mining plays an essential role in many areas and substantial research has been conducted on their analysis and applications. In our previous work [12], we used a simple but efficient Sequential Patterns Graph (SPG) to model the sequential patterns. The task to discover hidden Structural Pattern is based on our previous work and sequential patterns mining, conveniently named Post Sequential Patterns Mining. In this paper, in addition to stating this new mining problem, we define patterns such as branch pattern, iterative pattern, structural pattern, and concentrate on finding concurrent branch pattern. Concurrent branch pattern is thus one of the main forms of structural pattern and will play an important role in event-based data modelling.

Key words

Post Sequential Patterns Mining Sequential Patterns Graph Structural Pattern Concurrent Branch Patterns 


  1. 1.
    R. Agrawal and R. Srikant. Mining Sequential Patterns. Eleventh Int’l Conference on Data Engineering, Taipei, Taiwan. IEEE Computer Society Press, pages 3–14, March 1995.Google Scholar
  2. 2.
    R. Agrawal, D. Gunopulos and F. Leymann. Mining Process Models from Workflow Logs. Proceedings of the Sixth International Conference on Extending Database Technology (EDBT), 1998.Google Scholar
  3. 3.
    J.E. Cook and A.L Wolf. Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology, 7(3), pages 215–249, 1998.CrossRefGoogle Scholar
  4. 4.
    J.E. Cook and A.L Wolf. Software Process Validation: Quantitatively Measuring the correspondence of a Process to a Model. ACM Transactions on Software Engineering and Methodology, 8(2), pages 147–176, 1999.CrossRefGoogle Scholar
  5. 5.
    M. Garofalakis, R. Rastogi, and K. Shim. SPRIT: Sequential Pattern Mining with Regular Expression Constraints. Twenty-fifth International Conference on Very Large Data Bases, Edinburgh, Scotland, UK, Morgan Kaufmann, pages 223–234, September 1999.Google Scholar
  6. 6.
    A. Gionis, T. Kujala and H. Mannila. Fragments of Order, SIGKDD’03, pages 129–136, Washington, DC, USA, August 2003.Google Scholar
  7. 7.
    J. Herbst. A Machine Learning Approach to Workflow Management. Proceedings of European Conference on Machine Learning (ECML-2000), Lecture Notes in Artificial Intelligence No. 1810, pages 183–194, 2000.Google Scholar
  8. 8.
    J. Herbst. Dealing with Concurrency in Workflow Induction. In Proceedings of the 7th European Concurrent Engineering Conference, Society for Computer Simulation (SCS), pages 169–174, 2000.Google Scholar
  9. 9.
    J. Herbst and D. Karagiannis. Integrating Machine Learning and Workflow Management to Support Acquisition and Adaptation of Workflow Models. International Journal of Intelligent Systems in Accounting, Finance and Management, 9: pages 67–92, 2000.CrossRefGoogle Scholar
  10. 10.
    M. Y. Lin and S.Y. Lee. Fast discovery of sequential patterns by memory indexing. DaWaK, pages. 150–160, 2002.Google Scholar
  11. 11.
    J. Lu, O. Adjei, X.F. Wang and F. Hussain. Sequential Patterns Modeling and Graph Pattern Mining. the forthcoming IPMU Conference, Perugia, July, 4–9, 2004.Google Scholar
  12. 12.
    J. Lu, X.F. Wang, O. Adjei and F. Hussain. Sequential Patterns Graph and its Construction Algorithm. Chinese Journal of Computers, 2004 Vol. 6.Google Scholar
  13. 13.
    H. Mannila and D. Rusakov. Decomposing Event Sequences into Independent Components, In V. Kumar and R. Grossman, editors, the First SIAM Conference on Data Mining, Proc., pages 1–17, SIAM, 2001.Google Scholar
  14. 14.
    J. Pei, J.W. Han, B. Mortazavi-Asl,. and H. Pinto. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. Seventh Int’l Conference on Data Engineering, Heidelberg, Germany, 2001.Google Scholar
  15. 15.
    R. Srikant, R. Agrawal. Mining Sequential Patterns: Generalizations and Performance Improvements. Fifth Int’l on Extending Database Technology, EDBT, vol. 1057, Avigon, France, pages 3–17, March 1996.Google Scholar
  16. 16.
    M.J. Zaki. SPADE: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1/2), pages 31–60, 2001.zbMATHCrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2005

Authors and Affiliations

  • Jing Lu
    • 1
  • Osei Adjei
    • 2
  • Weiru Chen
    • 1
  • Jun Liu
    • 1
  1. 1.School of Computer Science and TechnologyShenyang Institute of Chemical TechnologyShenyangChina
  2. 2.Deparfmenf of Computing and Information SystemsUniversity of LutonLutonUK

Personalised recommendations