Abstract
Nowadays time series mining has been taken into account in many various application domains. One of the most popular mining tasks in the existing works is the frequent pattern mining task on time series databases. Periodic patterns in a time series are often examined in this task. Such patterns help us understand more the corresponding object observed on a regular basis. As we extend our consideration to a group of many different objects to find out their common behaviors repeating over time, we need a pattern type to be more informative and thus a solution to discover the hidden patterns. Therefore, our work aims at so-called interval-based sequential patterns frequently in a time series database. We also provide two different solutions to mining such frequent patterns: the first one based on the existing ARMADA solution with the additional preprocessing and post-processing and the second one based on our new FITSPATS algorithm with the use of stems as suffix expansion and a temporal pattern tree. Experimental results have shown that our solutions are capable of discovering the frequent interval-based sequential patterns in a time series database and the FITSPATS algorithm is more effective and efficient for the task.
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References
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of ICDE, pp. 3–14 (1995)
Assfalg, J., Bernecker, T., Kriegel, H.-P., Kröger, P., Renz, M.: Periodic pattern analysis in time series databases. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds.) DASFAA 2009. LNCS, vol. 5463, pp. 354–368. Springer, Heidelberg (2009)
Batal, I., Fradkin, D., Harrison, J., Mörchen, F., Hauskrecht, M.: Mining recent temporal patterns for event detection in multivariate time series data. In: Proceedings of KDD, pp. 280–288 (2012)
Chanda, K., Saha, S., Nishi, M.A., Samiullah, M., Ahmed, C.F.: An efficient approach to mine flexible periodic patterns in time series databases. Eng. Appl. Artif. Intell. 44, 46–63 (2015)
Chen, Y.-C., Jiang, J.-C., Peng, W.-C., Lee, S.-Y.: An efficient algorithm for mining time interval-based patterns in large databases. In: Proceedings of CIKM, pp. 49–58 (2010)
Dorr, D.H., Denton, A.M.: Establishing relationships among patterns in stock market data. Data Knowl. Eng. 68, 318–337 (2009)
Hirate, Y., Yamana, H.: Generalized sequential pattern mining with item intervals. J. Comput. 1(3), 51–60 (2006)
Mörchen, F., Ultsch, A.: Efficient mining of understandable patterns from multivariate interval time series. Data Min. Knowl. Disc. 15, 181–215 (2007)
Nishi, M.A., Ahmed, C.F., Samiullah, M., Jeong, B.-S.: Effective periodic pattern mining in time series databases. Expert Syst. Appl. 40, 3015–3027 (2013)
Rashee, F., Alshalalfa, M., Alhajj, R.: Efficient periodicity mining in time series databases using suffix trees. IEEE Trans. Knowl. Data Eng. 23(1), 79–94 (2011)
Shameem, M.R., Naseem, M.R., Subanivedhi, N.K., Sethukkarasi, R.: A dynamic approach for mining generalised sequential patterns in time series clinical data sets. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds.) ACITY 2012. AISCC, vol. 177, pp. 667–674. Springer, Heidelberg (2013)
Winarko, E., Roddick, J.F.: ARMADA – an algorithm for discovering richer relative temporal association rules from interval – based data. Data Knowl. Eng. 63, 76–90 (2007)
Wu, Y., Wang, L., Ren, J., Ding, W., Wu, X.: Mining sequential patterns with periodic wildcard gaps. Appl. Intell. 41, 99–116 (2014)
Xylogiannopoulos, K.F., Karampelas, P., Alhajj, R.: Analyzing very large time series using suffix arrays. Appl. Intell. 41, 941–955 (2014)
Zhang, M., Kao, B., Cheung, D.W., Yip, K.Y.: Mining periodic patterns with gap requirement from sequences. ACM Trans. Knowl. Disc. Data (TKDD) 1(2), August 2007
Zhou, H., Hirasawa, K.: Traffic conduction analysis model with time series rule mining. Expert Syst. Appl. 41(14), 6524–6535 (2014)
Zhuang, D.E.H., Li, G.C.L., Wong, A.K.C.: Discovery of temporal associations in multivariate time series. IEEE Trans. Knowl. Data Eng. 26(12), 2969–2982 (2014)
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This paper is funded by Ho Chi Minh City University of Technology, Vietnam National University at Ho Chi Minh City, Vietnam, under the grant number TNCS-2015-KHMT-07.
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Tran, P.T.B., Chau, V.T.N., Anh, D.T. (2015). Towards Efficiently Mining Frequent Interval-Based Sequential Patterns in Time Series Databases. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_12
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DOI: https://doi.org/10.1007/978-3-319-26181-2_12
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