Abstract
Serial episode is a type of temporal frequent pattern in sequence data. In this paper we compare the performance of serial episode discovering algorithms. Many different algorithms have been proposed to discover different types of episodes for different applications. However, it is unclear which algorithm is more efficient for discovering different types of episodes. We compare Minepi and WinMiner which discover serial episodes defined by minimal occurrence of subsequence. We find Minepi cannot discover all minimal occurrences of serial episodes as the literature, which proposed it, claimed. We also propose an algorithm Ap-epi to discover minimal occurrences of serial episode, which is a complement of Minepi. We propose an algorithm NOE-WinMiner which discovers non-overlapping episodes and compare it with an existing algorithm. Extensive experiments demonstrate that Ap-epi outperforms Minepi(fixed) when the minimum support is large and NOE-WinMiner beats the existing algorithm which discovers non-overlapping episodes with constraints between the two adjacent events.
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
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequences. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD 1995), Montréal, Canada, pp. 210–215 (1995)
Mannila, H., Toivonen, H.: Discovering generalized episodes using minimal occurrences. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), Portland, OR, pp. 146–151 (1996)
Méger, N., Rigotti, C.: Constraint-Based Mining of Episode Rules and Optimal Window Sizes. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 313–324. Springer, Heidelberg (2004)
Laxman, S., Sastry, P.S., Unnikrishnan, K.P.: A fast algorithm for finding frequent episodes in event streams. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, August 12-15 (2007)
Han, J., Pei, J.: Mining Frequent Patterns by Pattern-Growth: Methodology and Implications. ACM SIGKDD Explorations (Special Issue on Scaleble Data Mining Algorithms)Â 2(2) (December 2000)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation (PDF). In: Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD 2000), Dallas, TX ( May 2000)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., United States, May 25-28, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, March 06-10, pp. 3–14 (1995)
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)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)
Suzuki, E.: Interestingness Measures-Limits, Desiderata, and Recent Results. In: QIMIE 2009 (2009)
Ng, A., Fu, A.: Mining Frequent Episodes for Relating Financial Events and Stock Trend. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, pp. 27–39. Springer, Heidelberg (2003)
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
Wu, J., Wan, L., Xu, Z. (2012). Algorithms to Discover Complete Frequent Episodes in Sequences. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds) New Frontiers in Applied Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 7104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28320-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-28320-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28319-2
Online ISBN: 978-3-642-28320-8
eBook Packages: Computer ScienceComputer Science (R0)