Abstract
Sequential patterns are useful in many areas such as biomedical sequence analysis, web browsing log analysis, and historical banking transaction log analysis. Distinguishing sequential patterns can help characterize the differences between two or more sets/classes of sequences, and can be used to understand those sequence sets/classes and to identify informative features for classification and so on. However, previous studies have not considered how to mine distinguishing sequential patterns from event sequences, where each event in a sequence has an associated timestamp. To fill that gap, this paper considers the mining of distinguishing temporal event patterns (DTEP) from event sequences. After discussing the challenges on DTEP mining, we present DTEP-Miner, a mining method with various pruning techniques, for mining DTEPs with top-k contrast scores. Our empirical study using both real data and synthetic data demonstrates that DTEP-Miner is effective and efficient.
This work was supported in part by NSFC 61572332, the Fundamental Research Funds for the Central Universities 2016SCU04A22, and the China Postdoctoral Science Foundation 2016T90850, 2014M552371.
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
References
Dong, G., Pei, J.: Sequence Data Mining. Springer, Heidelberg (2007)
Dong, G., Bailey, J. (eds.): Contrast Data Mining: Concepts, Algorithms, and Applications. CRC Press, Boca Raton (2013)
Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996). doi:10.1007/BFb0014140
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Prefixspan: mining sequential patterns by prefix-projected growth. In: Proceedings of the 17th IEEE International Conference on Data Engineering, ICDE, pp. 215–224 (2001)
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining, KDD, pp. 429–435 (2002)
Ji, X., Bailey, J., Dong, G.: Mining minimal distinguishing subsequence patterns with gap constraints. Knowl. Inf. Syst. 11(3), 259–286 (2007)
Deng, K., Zaïane, O.R.: Contrasting sequence groups by emerging sequences. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS (LNAI), vol. 5808, pp. 377–384. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04747-3_29
Wang, X., Duan, L., Dong, G., Yu, Z., Tang, C.: Efficient mining of density-aware distinguishing sequential patterns with gap constraints. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8421, pp. 372–387. Springer, Heidelberg (2014). doi:10.1007/978-3-319-05810-8_25
Yang, H., Duan, L., Dong, G., Nummenmaa, J., Tang, C., Li, X.: Mining itemset-based distinguishing sequential patterns with gap constraint. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M.A. (eds.) DASFAA 2015. LNCS, vol. 9049, pp. 39–54. Springer, Heidelberg (2015). doi:10.1007/978-3-319-18120-2_3
Li, T., Ma, S.: Mining temporal patterns without predefined time windows. In: Proceedings of the 4th IEEE International Conference on Data Mining, ICDM. 451–454 (2004)
Tang, L., Li, T., Shwartz, L.: Discovering lag intervals for temporal dependencies. In: Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining, KDD, pp. 633–641 (2012)
Ma, S., Hellerstein, J.L.: Mining mutually dependent patterns. In: Proceedings of the 1st IEEE International Conference on Data Mining, ICDM, pp. 409–416 (2001)
Ma, S., Hellerstein, J.L.: Mining partially periodic event patterns with unknown periods. In: Proceedings of the 17th IEEE International Conference on Data Engineering, ICDE, 205–214 (2001)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1(3), 259–289 (1997)
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
Wu, S., Chen, Y.: Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events. Data Knowl. Eng. 68(11), 1309–1330 (2009)
Mörchen, F., Ultsch, A.: Efficient mining of understandable patterns from multivariate interval time series. Data Min. Knowl. Discov. 15(2), 181–215 (2007)
Shah, C.C., Zhu, X., Khoshgoftaar, T.M., Beyer, J.: Contrast pattern mining with gap constraints for peptide folding prediction. In: Proceedings of the 21st International Florida Artificial Intelligence Research Society Conference, FLAIRS. pp. 95–100 (2008)
Rymon, R.: Search through systematic set enumeration. In: Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning, KR, pp. 539–550 (1992)
Lichman, M.: UCI machine learning repository (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Duan, L., Yan, L., Dong, G., Nummenmaa, J., Yang, H. (2017). Mining Top-k Distinguishing Temporal Sequential Patterns from Event Sequences. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-55699-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55698-7
Online ISBN: 978-3-319-55699-4
eBook Packages: Computer ScienceComputer Science (R0)