Abstract
Sequential generator pattern mining is an important task in data mining. Sequential generator patterns used together with closed sequential patterns can provide additional information that closed sequential patterns alone are not able to provide. In this paper, we proposed an algorithm called MSGPs, which based on the characteristics of sequential generator patterns and sequence extensions by doing depth-first search on the prefix tree, to find all of the sequential generator patterns. This algorithm uses a vertical approach to listing and counting the support, based on the prime block encoding approach of the prime factorization theory to represent candidate sequences and determine the frequency for each candidate. Experimental results showed that the proposed algorithm is effective.
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
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. of 11th Int’l Conf. Data Engineering, pp. 3–14 (1995)
Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential pattern mining using a bitmap representation. In: Proc. of ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, pp. 429–435 (2002)
Chang, L., Wang, T., Yang, D., Luan, H., Tang, S.: Efficient algorithms for incremental maintenance of closed sequential patterns in large databases. Data and Knowledge Engineering 68(1), 68–106 (2009)
Gao, C.C., Wang, J.Y., He, Y.K., Zhou, L.Z.: Efficient mining of frequent sequence generators. In: Proc. of the 17th International Conference on World Wide Web, Beijing, China, pp. 1051–1052 (2008)
Gouda, K., Hassaan, M., Zaki, M.J.: PRISM: A primal-encoding approach for frequent sequence mining. Journal of Computer and System Sciences 76(1), 88–102 (2010)
Huang, G., Yang, F., Hu, C., Ren, J.: Fast discovery of frequent closed sequential patterns based on positional data. In: Proc. of the 9th International Conference on Machine Learning and Cybernetics, Qingdao, vol. 1, pp. 444–449 (2010)
Li, J., Li, H., Wong, L., Pei, J., Dong, G.: Minimum description length (MDL) principle: Generators are preferable to closed patterns. In: Proc. of the 21th National Conference on Artificial Intelligence, AAAI 2006, Boston, Massachusetts, USA, pp. 409–414 (2006)
Lo, D., Khoo, S.-C., Li, J.: Mining and ranking generators of sequential patterns. In: SIAM Conference on Data Mining, SDM 2008, Atlanta, Georgia, USA, pp. 553–564 (2008)
Luo, C., Chung, S.M.: A scalable algorithm for mining maximal frequent sequences using a sample. Knowledge and Information Systems 15(2), 149–179 (2008)
Nguyen, N.T.: A Method for Ontology Conflict Resolution and Integration on Relation Level. Cybernetics and Systems 38(8), 781–797 (2007)
Pei, J., et al.: Mining sequential patterns by pattern-growth: The PrefixSpan approach. IEEE Transaction on Knowledge and Data Engineering 16(10), 1424–1440 (2004)
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)
Wang, J., Han, J.: BIDE: Efficient mining of frequent closed sequences. In: Proc. of the 20th Int’ Conf. on Data Engineering, ICDE 1995, pp. 79–91. IEEE Computer Society Press (2004)
Wang, J., Han, J., Li, C.: Frequent closed sequence mining without candidate maintenance. IEEE Transaction on Knowledge and Data Engineering 19(8), 1024–1056 (2007)
Yan, X., Han, J., Afshar, R.: CloSpan: Mining closed sequential patterns in large datasets. In: Proc. of the 3rd SIAM International Conference on Data Mining, pp. 166–177. SIAM Press, San Francisco (2003)
Yia, S.W., Zhao, T.H., Zhang, Y.Y., Ma, S.L., Che, Z.B.: An effective algorithm for mining sequential generators. In: Procedia Engineering, CEIS 2011, vol. 15, pp. 3653–3657 (2011)
Zaki, M.J.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning Journal 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
Pham, TT., Luo, J., Hong, TP., Vo, B. (2012). MSGPs: A Novel Algorithm for Mining Sequential Generator Patterns. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34707-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-34707-8_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34706-1
Online ISBN: 978-3-642-34707-8
eBook Packages: Computer ScienceComputer Science (R0)