Abstract
The discovery of repeated subsequences, time series motifs, is a problem which has great utility for several higher-level data mining tasks, including classification, clustering, forecasting and rule discovery. In recent years there has been significant research effort spent on efficiently discovering these motifs in static time series data. However, for many applications, the streaming nature of time series demands a new kind of methods for discovery of time series motifs. In this paper, we develop a new method for motif discovery in streaming time series. In this method we use significant extreme points to determine motif candidates and then cluster motif candidates by BIRCH algorithm. The method is very effective not only for large time series data but also for streaming environment since it needs only one-pass of scan through the whole data.
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References
Castro, N., Azevedo, P.J.: Multiresolution Motif Discovery in Time Series. In: Proc. of the SIAM Int. Conf. on Data Mining, SDM 2010, Columbus, Ohio, USA, April 29-May 1 (2010)
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic Discovery of Time Series Motifs. In: Proc. of 9th Int. Conf. on Knowledge Discovery and Data Mining (KDD 2003), pp. 493–498 (2003)
Gruber, C., Coduro, M., Sick, B.: Signature verification with dynamic RBF network and time series motifs. In: Proc. of 10th International Workshop on Frontiers in Hand Writing Recognition (2006)
Keogh, E., Lin, J.: Clustering of Time-Series Subsequences is Meaningless. Implications for previous and future research. Knowl. Inf. Syst. 8(2), 154–177 (2005)
Li, Y., Lin, J., Oates, T.: Visualizing Variable-Length Time Series Motifs. In: SDM 2012, pp. 895–906 (2012)
Lin, J., Keogh, E., Patel, P., Lonardi, S.: Finding Motifs in Time Series. In: Proc. of the 2nd Workshop on Temporal Data Mining, at the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002)
Mueen, A., Keogh, E., Zhu, Q., Cash, S., Westover, B.: Exact Discovery of Time Series Motif. In: Proc. of 2009 SIAM International Conference on Data Mining, pp. 1–12 (2009)
Mueen, A., Keogh, E.: Online Discovery and Maintenance of Time Series Motif. In: Proc. of ACM SIGKDD 2010, pp. 1089–1098 (2010)
Mueen, A., Keogh, E., Zhu, Q., Cash, S., Bigdely-Shamlo, N.: Finding Time Series Motifs in Disk-Resident Data. In: Proc. of IEEE International Conference on Data Mining, ICDM, pp. 367–376 (2009)
Pratt, K.B., Fink, E.: Search for patterns in compressed time series. International Journal of Image and Graphics 2(1), 89–106 (2002)
Tanaka, Y., Iwamoto, K., Uehara, K.: Discovery of Time Series Motif from Multi-Dimensional Data based on MDL Principle. Machine Learning 58(2-3), 269–300 (2005)
Tang, H., Liao, S.: Discovering Original Motifs with Different Lengths from Time Series. Knowledge-based System 21(7), 666–671 (2008)
Truong, C.D., Anh, D.T.: An Efficient Method for Discovering Motifs in Large Time Series. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013, Part I. LNCS (LNAI), vol. 7802, pp. 135–145. Springer, Heidelberg (2013)
Xi, X., Keogh, E., Li, W., Mafra-neto, A.: Finding Motifs in a Database of Shapes. In: SDM 2007. LNCS, vol. 4721, pp. 249–260. Springer, Heidelberg (2007)
Yang, Q., Wu, X.: 10 Challenging Problems in Data Mining Research. Intl. Jrnl. of Information Technology & Decision Making 5(4), 597–604 (2006)
Yankov, D., Keogh, E., Medina, J., Chiu, B., Zordan, V.: Detecting Time Series Motifs under Uniform Scaling. In: Proc. of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), pp. 844–853 (2007)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: An efficient data clustering method for very large databases. SIGMOD Rec. 25(2), 103–114 (1996)
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Truong, C.D., Anh, D.T. (2014). An Efficient Method for Discovering Motifs in Streaming Time Series Data. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-319-02741-8_20
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DOI: https://doi.org/10.1007/978-3-319-02741-8_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02740-1
Online ISBN: 978-3-319-02741-8
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