Abstract
Sequential pattern mining is a well-studied data mining task with wide applications. However, fine-tuning the minsup parameter of sequential pattern mining algorithms to generate enough patterns is difficult and time-consuming. To address this issue, the task of top-k sequential pattern mining has been defined, where k is the number of sequential patterns to be found, and is set by the user. In this paper, we present an efficient algorithm for this problem named TKS (Top-K Sequential pattern mining). TKS utilizes a vertical bitmap database representation, a novel data structure named PMAP (Precedence Map) and several efficient strategies to prune the search space. An extensive experimental study on real datasets shows that TKS outperforms TSP, the current state-of-the-art algorithm for top-k sequential pattern mining by more than an order of magnitude in execution time and memory.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proc. Int. Conf. on Data Engineering, pp. 3–14 (1995)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Trans. Knowledge and Data Engineering 16(10), 1–17 (2001)
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential PAttern mining using a bitmap representation. In: Proc. 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2002), Edmonton, Alberta, July 23-26, pp. 429–435 (2002)
Tzvetkov, P., Yan, X., Han, J.: TSP: Mining Top-k Closed Sequential Patterns. Knowledge and Information Systems 7(4), 438–457 (2005)
Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys 43(1), 1–41 (2010)
Fournier-Viger, P., Wu, C.-W., Tseng, V.S.: Mining Top-K Association Rules. In: Kosseim, L., Inkpen, D. (eds.) Canadian AI 2012. LNCS, vol. 7310, pp. 61–73. Springer, Heidelberg (2012)
Kun Ta, C., Huang, J.-L., Chen, M.-S.: Mining Top-k Frequent Patterns in the Presence of the Memory Constraint. VLDB Journal 17(5), 1321–1344 (2008)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publ., San Francisco (2006)
Cormen, T.H., Leiserson, C.E., Rivest, R., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fournier-Viger, P., Gomariz, A., Gueniche, T., Mwamikazi, E., Thomas, R. (2013). TKS: Efficient Mining of Top-K Sequential Patterns. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-53914-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53913-8
Online ISBN: 978-3-642-53914-5
eBook Packages: Computer ScienceComputer Science (R0)