Mining Cyclically Repeated Patterns

  • Ismail H. Toroslu
  • Murat Kantarcioglu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)


In sequential pattern mining, the support of the sequential pattern for the transaction database is defined only by the fraction of the customers supporting this sequence, which is known as the customer support. In this paper, a new parameter is introduced for each customer, called as repetition support, as an additional constraint to specify the minimum number of repetitions of the patterns by each customer. We call the patterns discovered using this technique as cyclically repeated patterns. The additional parameter makes the new mining technique more efficient and also helps discovering more useful patterns by reducing the number of patterns searched. Also, ordinary sequential pattern mining can be represented as a special case of the cyclically repeated pattern mining. In this paper, we introduce the concept of mining cyclically repeated patterns, we describe the related algorithms, and at the end of the paper we give some performance results.


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  1. 1.
    R. Agrawal, C. Faloutsos, A. Swami: “Efficient similarity search in sequence databases”, Proc. of the 4th Int’l Conference on Foundations of Data Organization and Algorithms, Chicago, Oct. 1993, Also in Lecture Notes in Computer Science 730, Springer Verlag, pp. 69–84, 1993.Google Scholar
  2. 2.
    R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases”, Proc. of the ACM SIGMOD Conference on Management of Data, pp. 207–216, Washington D.C., May 1993.Google Scholar
  3. 3.
    R. Agrawal, R. Srikant, “Mining sequential patterns”, Proc. of the Int’l Conference on Data Engineering (ICDE), pp. 3–14, Taipei, Taiwan, March 1995.Google Scholar
  4. 4.
    C. Bettini, X.S. Wang, S. Jajodia, “Mining temporal relationships with multiple granularities in time sequences”, Data Engineering Bulletin, Vol. 21, pp. 32–38, 1998.Google Scholar
  5. 5.
    T.G. Dietterich, R.S. Michalski, “Discovering patterns in sequence of events”, Artificial Intelligence, vol. 25, pp. 187–232, 1985.CrossRefGoogle Scholar
  6. 6.
    J. Han, “Data mining”, 5Encyclopedia of Distributed Computing, Eds. J. Urban and P. Dasgupta, Kluwer Academic Publishers, 1999.Google Scholar
  7. 7.
    J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, M-C. Hsu, Freespan: Frequent pattern-projected sequential pattern mining”, In Proc. 2000 Int. Conf. Knowledge Discovery and Data Mining (KDD’00), pp. 355–359, Boston, MA, Aug. 2000.Google Scholar
  8. 8.
    J. Han, J. Pei, Y. Yin, “Mining frequent patterns without candidate generation”, In Proc. 2000 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’00), pp. 1–12, Dallas, TX, May 2000.Google Scholar
  9. 9.
    M. Garofolakis, R. Rastogi, K. Shim, “Spirit: Sequential pattern mining with regular expression constraints”, In Proc. 1999 Int. Conf. Very Large Data Bases (VLDB’99), pp. 223–224, Edinburgh, UK, Sept. 1999.Google Scholar
  10. 10.
    R. Srikant, R. Agrawal, “Mining sequential patterns: generalizations and performance improvements”. pp. 3-17, Proc. of the Fifth Int’l Conference on Extending Database Technology (EDBT), Avignon, France, March 1996.Google Scholar
  11. 11.
    S. Wu, U. Manber, “Fast text searching allowing errors”, Communications of the ACM, vol. 35,no. 10, pp. 83–91, Oct. 1992.CrossRefGoogle Scholar
  12. 12.
    M.J. Zaki, “Efficient Enumeration of Frequent Sequences”, 7th International Conference on Information and Knowledge Management, pp 68–75, Washington DC, November 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ismail H. Toroslu
    • 1
  • Murat Kantarcioglu
    • 2
  1. 1.Department of Computer ScienceUniversity of Central FloridaOrlando
  2. 2.Department of Computer SciencePurdue UniversityWest Lafayette

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