Abstract
In this paper, a novel algorithm which is called CIPMPBE (Closed Iterative Pattern Miner via Prime-Block Encoding) is proposed to mine closed iterative patterns. CIPMPBE is composed of three separated steps. In the first step, the positional information of all frequent i-sequences is generated. In the second step, the positional information of all frequent i-sequences and all instances of frequent (i-1)-iterative patterns are used to obtain the positional information of all instances of frequent i-iterative patterns. In the third step, the positional information of all instances of frequent i-iterative pattern is used to obtain the positional information of the entire closed i-iterative pattern and get back to the first step to generate the positional information of closed (i+1)-iterative pattern. For effective testing, a set of experiments were performed. The results of these experiments show that the time efficiency of CIPMPBE is better than that of CLIPER (CLosed Iterative Pattern minER).
The work was supported by the Fundamental Research Funds for Central Universities under grant No.lzugbky-2010-91.
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: 11th International Conference on Data Engineering (ICDE 1995), vol. 6, pp. 3–14 (1995)
ITU-T. ITU-T Recommendation Z.120: Message Sequence Chart (MSC) (1999)
Lo, D., Khoo, S.-C., Liu, C.: Efficient Mining of Iterative Patterns for Software Specification Discovery. In: KDD (2007)
Gouda, K., Hassaan, M., Zaki, M.J.: PRISM: An effective approach for frequent sequence mining via prime-block encoding. Journal of Computer and System Science (2009)
Xu, Y., Zhang, L., Ma, Z., Li, L., Chen, X.: Mining Sequential Pattern Using DF2Ls. In: Fifth International Conference on Fuzzy System and Knowledge Discovery (2008)
Mannial, H., Toivonenm, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. DMKD 1, 259–289 (2006)
Wang, J., Han, J.: BIDE: Efficient mining of frequent closed sequences. In: ICDE (2004)
Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential Pattern Mining using A Bitmap Representation. In: Proceedings of ACM SIGKDD on Knowledge discovery and data mining, pp. 429–435 (2002)
Olender, K., Osterweil, L.: Ceil: A sequencing constraint language for automatic static analysis generation. IEEE TSE 16, 268–280 (1990)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE (2001)
Gouda, K., Hassaan, M., Zaki, M.J.: PRISM: A prime-encoding approach for frequent sequence mining. In: IEEE Int’l Conf. on Data Mining (October 2007)
Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.-C.: Freespan: Frequent pattern-projected sequentisl pattern mining. In: KDD (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, Z., Ding, Z., Xu, Y. (2011). Efficient Closed Iterative Patterns Mining Algorithm via Prime-Block Encoding. In: Wang, Y., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent and Soft Computing, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25664-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-25664-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25663-9
Online ISBN: 978-3-642-25664-6
eBook Packages: EngineeringEngineering (R0)