Mining Frequent Bipartite Episode from Event Sequences

  • Takashi Katoh
  • Hiroki Arimura
  • Kouichi Hirata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5808)

Abstract

In this paper, first we introduce a bipartite episode of the form AB for two sets A and B of events, which means that every event of A is followed by every event of B. Then, we present an algorithm that finds all frequent bipartite episodes from an input sequence without duplication in O(|Σ| ·N) time per an episode and in O(|Σ|2n) space, where Σ is an alphabet, N is total input size of \(\mathcal S\), and n is the length of S. Finally, we give experimental results on artificial and real sequences to evaluate the efficiency of the algorithm.

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References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. 20th VLDB, pp. 487–499 (1994)Google Scholar
  2. 2.
    Arimura, H.: Efficient algorithms for mining frequent and closed patterns from semi-structured data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 2–13. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Arimura, H., Uno, T.: A polynomial space and polynomial delay algorithm for enumeration of maximal motifs in a sequence. In: Deng, X., Du, D.-Z. (eds.) ISAAC 2005. LNCS, vol. 3827, pp. 724–737. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Avis, D., Fukuda, K.: Reverse search for enumeration. Discrete Applied Mathematics 65, 21–46 (1996)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Katoh, T., Hirata, K.: Mining frequent elliptic episodes from event sequences. In: Proc. 5th LLLL, pp. 46–52 (2007)Google Scholar
  6. 6.
    Katoh, T., Hirata, K.: A simple characterization on serially constructible episodes. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 600–607. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Katoh, T., Arimura, H., Hirata, K.: A Polynomial-Delay Polynomial-Space Algorithm for Extracting Frequent Diamond Episodes from Event Sequences. In: Theeramunkong, T., et al. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 172–183. Springer, Heidelberg (2009)Google Scholar
  8. 8.
    Katoh, T., Hirata, K., Harao, M.: Mining sectorial episodes from event sequences. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds.) DS 2006. LNCS (LNAI), vol. 4265, pp. 137–148. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Katoh, T., Hirata, K., Harao, M.: Mining frequent diamond episodes from event sequences. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617, pp. 477–488. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1, 259–289 (1997)CrossRefGoogle Scholar
  11. 11.
    Pei, J., Wang, H., Liu, J., Wang, K., Wang, J., Yu, P.S.: Discovering frequent closed partial orders from strings. IEEE TKDE 18, 1467–1481 (2006)Google Scholar
  12. 12.
    Pei, J., Han, J., Mortazavi-Asi, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Mining sequential patterns by pattern-growth: The PrefixSpan approach. IEEE Trans. Knowledge and Data Engineering. 16, 1–17 (2004)CrossRefGoogle Scholar
  13. 13.
    Uno, T.: Two general methods to reduce delay and change of enumeration algorithms, NII Technical Report, NII-2003-004E (April 2003)Google Scholar
  14. 14.
    Zaki, M.J.: Scalable Algorithms for Association Mining. IEEE TKDE 12, 372–390 (2000)Google Scholar
  15. 15.
    Zaki, M.J., Hsiao, C.-J.: CHARM: An efficient algorithm for closed itemset mining. In: Proc. 2nd SDM, pp. 457–478. SIAM, Philadelphia (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Takashi Katoh
    • 1
  • Hiroki Arimura
    • 1
  • Kouichi Hirata
    • 2
  1. 1.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan
  2. 2.Department of Artificial IntelligenceKyushu Institute of TechnologyIizukaJapan

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