Using Partially-Ordered Sequential Rules to Generate More Accurate Sequence Prediction

  • Philippe Fournier-Viger
  • Ted Gueniche
  • Vincent S. Tseng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7713)


Predicting the next element(s) of a sequence is a research problem with wide applications such as stock market prediction, consumer product recommendation, and web link recommendation. To address this problem, an effective approach is to mine sequential rules from a set of training sequences to then use these rules to make predictions for new sequences. In this paper, we improve on this approach by proposing to use a new kind of sequential rules named partially-ordered sequential rules instead of standard sequential rules. Experiments on large click-stream datasets for webpage recommendation show that using this new type of sequential rules can greatly increase prediction accuracy, while requiring a smaller training set.


symbolic sequence prediction sequential rules partial order 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Antonie, M.-L., Chodos, D., Zaiane, O.: Variations on Associative Classifiers and Classification Results Analyses. In: Zhao, Y., Zhang, C., Cao, L. (eds.) Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction (2008)Google Scholar
  2. 2.
    Begleiter, R., El-Yaniv, R., Yona, G.: On Prediction Using Variable Order Markov Models. Journal of Artificial Intelligence Research 22, 385–421 (2004)MathSciNetMATHGoogle Scholar
  3. 3.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, San Francisco (2011)Google Scholar
  4. 4.
    Pei, J., et al.: Mining Sequential Patterns by Pattern Growth: The PrefixSpan Approach. IEE Trans. on Knowledge and Data Engineering. 16(11), 1420–1440 (2004)Google Scholar
  5. 5.
    Fournier-Viger, P., Wu, C.-W., Tseng, V.S., Nkambou, R.: Mining Sequential Rules Common to Several Sequences with the Window Size Constraint. In: Kosseim, L., Inkpen, D. (eds.) Canadian AI 2012. LNCS, vol. 7310, pp. 299–304. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Fournier-Viger, P., Tseng, V.S.: Mining Top-K Sequential Rules. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) ADMA 2011, Part II. LNCS, vol. 7121, pp. 180–194. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Fournier-Viger, P., Nkambou, R., Tseng, V.S.: RuleGrowth: Mining Sequential Rules Common to Several Sequences by Pattern-Growth. In: ACM SAC 2011, pp. 954–959. ACM Press (2011)Google Scholar
  8. 8.
    Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Fourth International Conference on Knowledge Discovery and Data Mining (KDD 1998), pp. 80–86. AAAI Press, New York (1998)Google Scholar
  9. 9.
    Liu, D.-R., Lai, C.-H.: A hybrid of sequential rules and collaborative filtering for product recommendation. Information Sciences 179, 3505–3519 (2009)CrossRefGoogle Scholar
  10. 10.
    Lo, D., Khoo, S.-C., Wong, L.: Non-redundant sequential rules Theory and algorithm. Information Systems 34(4-5), 438–453 (2009)CrossRefGoogle Scholar
  11. 11.
    Pérez-Ortiz, J.A., Calera-Rubio, J., Forcada, M.L.: Online Symbolic-Sequence Prediction with Discrete-Time Recurrent Neural Networks. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 719–724. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  12. 12.
    Pitman, A., Zanker, M.: An Empirical Study of Extracting Multidimensional Sequential Rules for Personalization and Recommendation in Online Commerce. In: 10th Intern. Conf. on Wirtschaftsinformatik (2011)Google Scholar
  13. 13.
    Sun, R., Giles, C.L.: Sequence Learning: From Recognition and Prediction to Sequential Decision Making. IEEE Intelligent Systems 16(4) (2001)Google Scholar
  14. 14.
    Zaki, M.J.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning 42(1/2), 31–60 (2001)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
  • Ted Gueniche
    • 1
  • Vincent S. Tseng
    • 2
  1. 1.Dept. of Computer ScienceUniversity of MonctonCanada
  2. 2.Dept. of Computer Science and Inf. Eng.National Cheng Kung UniversityTaiwan

Personalised recommendations