Abstract
At present, the existing incremental mining algorithms of sequential patterns can not make full use of the mining information in original database, the updated database is scanned many times and the projected databases for frequent items are constructed. In this paper, we propose an incremental mining algorithm of sequential patterns based on Sequence tree, called ISPBS. ISPBS uses the sequence tree (Stree) to store the mining information in the original database. Sequence tree is a novel data structure, it is similar in structure to the prefix tree. But the sequence tree stores all the sequences in the original database. When the database is updated, ISPBS only deals with the incremental sequences and adds the sequences to the sequence tree without constructing the projected database. It can find all the sequential patterns in the updated database by scanning the sequence tree. Experiments show that ISPBS outperforms PrefixSpan and IncSpan in time cost.
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
Zou, X., Zhao, L., Guo, J., Chen, X.: An advanced algorithm of frequent subgraph mining based on ADI. ICIC Express Letters 3, 639–644 (2009)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. Int. Conf. Data Engineering, pp. 3–14. IEEE Press, Taipei (1995)
Srikant, R., Agrawal, R.: Mining sequential patterns: generalization and performance improvements. In: Lect. Notes Comput. Science, vol. 1057, pp. 3–17 (1996)
Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learning 42, 31–60 (2001)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., et al.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proc. Int. Conf. Data Engineering, pp. 215–224. IEEE Press, Heidelberg (2001)
Cheng, H., Yan, X., Han, J.: IncSpan: incremental mining of sequential patterns in large database. In: KDD Proc. Tenth ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, pp. 527–532. Association for Computing Machinery Press, Seattle (2004)
Nguyen, S.N., Sun, X., Orlowska, M.E.: Improvements of IncSpan: Incremental Mining of Sequential Patterns in Large Database. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 442–451. Springer, Heidelberg (2005)
Chen, Y., Guo, J., Wang, Y., Xiong, Y., Zhu, Y.: Incremental Mining of Sequential Patterns Using Prefix Tree. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 433–440. Springer, Heidelberg (2007)
Ren, J., Sun, Y., Guo, S.: Incremental sequential pattern mining based on constraints. J. Comput. Inf. Systems 4, 571–576 (2008)
Lin, M.-Y., Hsueh, S.-C., Chan, C.-C.: Incremental discovery of sequential patterns using a backward mining approach. In: Proc. IEEE Int. Conf. Comput. Sci. Engineering, pp. 64–70. IEEE Press, Vancouver (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Liu, J., Yan, S., Wang, Y., Ren, J. (2012). Incremental Mining Algorithm of Sequential Patterns Based on Sequence Tree. In: Lee, G. (eds) Advances in Intelligent Systems. Advances in Intelligent and Soft Computing, vol 138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27869-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-27869-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27868-6
Online ISBN: 978-3-642-27869-3
eBook Packages: EngineeringEngineering (R0)