Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Begleiter, R., El-Yaniv, R., Yona, G.: On Prediction Using Variable Order Markov Models. Journal of Artificial Intelligence Research 22, 385–421 (2004)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, San Francisco (2011)
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)
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)
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)
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)
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)
Liu, D.-R., Lai, C.-H.: A hybrid of sequential rules and collaborative filtering for product recommendation. Information Sciences 179, 3505–3519 (2009)
Lo, D., Khoo, S.-C., Wong, L.: Non-redundant sequential rules Theory and algorithm. Information Systems 34(4-5), 438–453 (2009)
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)
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)
Sun, R., Giles, C.L.: Sequence Learning: From Recognition and Prediction to Sequential Decision Making. IEEE Intelligent Systems 16(4) (2001)
Zaki, M.J.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning 42(1/2), 31–60 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fournier-Viger, P., Gueniche, T., Tseng, V.S. (2012). Using Partially-Ordered Sequential Rules to Generate More Accurate Sequence Prediction. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-35527-1_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35526-4
Online ISBN: 978-3-642-35527-1
eBook Packages: Computer ScienceComputer Science (R0)