Abstract
Recently, the detection of a previously unknown, frequently occurring pattern has been regarded as a difficult problem. We call this pattern as “motif”. Many researchers have proposed algorithms for discovering the motif. However, if the optimal period length of the motif is not known in advance, we cannot use these algorithms for discovering the motif. In this paper, we attempt to dynamically determine the optimum period length using the MDL principle. Moreover, in order to apply this algorithm to the multi dimensional time-series, we transform the time-series into one dimensional time-series by using the Principal Component Analysis. Finally, we show experimental results and discuss the efficiency of our motif discovery algorithm.
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References
Gautam, D., King-lp, L., Heikki, M., Gopal, R., Padhraic, S.: Rule Discovery from Time Series. Proc. of the 4th Int’l Conference on Knowledge Discovery and Data Mining (1998) 16–22
Cyril, G., Peter, T., Egill, R., Arup, N.F., Kai, H.L.: On Clustering fMRI Time Series. NeuroImage 9 (1999) 298–310
Yu, J.X., K.Ng, M., Huang, J.Z.: Patterns Discovery Based on Time-Series Decomposition. Proc. of PAKDD’2001 (2001) 336–347
Berberidis, C., Vlahavas, I., Aref, W.G., Atallah, M., Elmagarmid, A.K.: On The Discovery of Weak Periodicities in Large Time Series. Proc. of PAKDD’2002 (2002) 51–61
Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding Motifs in Time Series. Proc. of the 2nd Workshop on Temporal Data Mining (2002) 53–68
Mori, T., Uehara, K.: Extraction of Primitive Motion And Discovery of Association Rules from Human Motion. Proc. of the 10th IEEE International Workshop on Robot and Human Communication (2001) 200–206
Rissanen, J.: Stochastic Complexity in Statistical Inquiry. Volume 15. World Scientific (1989)
Heras, D.B., Cabaleiro, J.C., Perez, V.B., Costas, P., Rivera, F.F.: Principal Component Analysis on Vector Computers. Proc. of VECPAR (1996) 416–428
Kalpakis, K., Gada, D., Puttagunta, V.: Distance Measures for Effective Clustering of ARIMA Time-Series. Proc. of the 2001 IEEE International Conference on Data Mining (2001) 273–280
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© 2003 Springer-Verlag Berlin Heidelberg
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Tanaka, Y., Uehara, K. (2003). Discover Motifs in Multi-dimensional Time-Series Using the Principal Component Analysis and the MDL Principle. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_22
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DOI: https://doi.org/10.1007/3-540-45065-3_22
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