Abstract
Motif discovery is a method for finding some previously unknown but frequently appearing patterns in time series. However, the high dimensionality and dynamic uncertainty of time series data lead to the main challenge for searching accuracy and effectiveness. In our paper, we propose a novel k-motifs discovery approach based on the Piecewise Linear Representation and the Skyline index, which is superior to traditional R-tree index. As the experimental results suggest, our approach is more accurate and effective than some other traditional methods.
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References
Lin, J., Keogh, E., Patel, P., Lonardi, S.: Finding motifs in time series. In: The 2nd Workshop on Temporal Data Mining. The 8th ACM International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, pp. 53–68 (2002)
Hailin, L., Chonghui, G.: Survey of feature representations and similarity measurements in time series data mining. Appl. Res. Comput. 30, 1285–1291 (2013)
Mueen, A., Keogh, E., Zhu, Q., Cash, S., Westover, B.: Exact discovery of time series motifs. In: Proceedings of SIAM International Conference on Data Mining, pp. 473–484 (2009)
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: The 9th International Conference On Knowledge Discovery And Data Mining, pp. 493–498 (2003)
Dazhuo, Z., Minqiang, L.: Time series segmentation based on series importance point. Comput. Eng. 34, 14–16 (2008)
Pratt, K.B., Fink, E.: Search for patterns in compressed time series. J. Image Graph. 2, 86–106 (2002)
Son, N.T., Anh, D.T.: Discovery of time series k-motifs based on multidimensional index. J. Knowl. Inf. Syst. 46(1), 59–86 (2016)
Cun, J., Shijun, L., Chenglei, Y.: The 20th IEEE International Conference on Computer Supported Cooperative Work in Design, Nanchang, Jiangxi, China (2016, in press)
Li, Q., Lopez, I.F.V., Moon, B.: Skyline index for time series data. IEEE Trans. Knowl. Data Eng. 16(6), 669–684 (2004)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3, 263–286 (2001)
Keogh, E., Zhu, Q., Hu, B., Hao, Y., Xi, X., Wei, L., Ratanamahatana, C.A.: The UCR time series classification/clustering homepage (2011)
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© 2016 Springer International Publishing Switzerland
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Hu, Y., Ji, C., Jing, M., Li, X. (2016). A K-Motifs Discovery Approach for Large Time-Series Data Analysis. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_53
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DOI: https://doi.org/10.1007/978-3-319-45817-5_53
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