Asynchronism-based principal component analysis for time series data mining

Expert Systems with Applications

Volumn 41, Issue 6, 2014, Pages 2842-2850

  Li, Hailin ^a  

^a HUAQIAO UNIVERSITY   (China)

Author keywords

Asynchronous correlation; Covariance matrix; Dynamic time warping; Principal component analysis; Time series data mining

Indexed keywords

ASYNCHRONOUS METHODS; CORRELATION COEFFICIENT; DIMENSIONALITY REDUCTION; DYNAMIC TIME WARPING; ON DYNAMICS; TIME SERIES DATA MINING; UNIVARIATE TIME SERIES;

COVARIANCE MATRIX; DATA MINING; PRINCIPAL COMPONENT ANALYSIS;

TIME SERIES;

EID: 84890551968     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.10.019     Document Type: Article

References (43)

1. Agrawal, R.; Faloutsos, C. & Swami, A. (1993). Efficient similarity search in sequence databases. In Proceedings of the fourth international conference on foundations of data organization and algorithms (pp. 69-84).
2. Z. Bankó, and J. Abonyi Correlation based dynamic time warping of multivariate time series Expert Systems with Applications 39 2012 12814 12823
3. R. Baragona, and F. Battaglia Outliers detection in multivariate time series by independent component analysis Neural Computation Archive 19 7 2007 1962 1984
4. Chu,S.; Keogh, E.; Hart, D.; Pazzani, M. (2002). Iterative deepening dynamic time warping for time series. In Proceedings of the second SIAM international conference on data mining (pp. 195-212).
5. A. Cichocki, and S. Amari Adaptive blind signal and image processing 2002 John Wiley
6. P. Esling, and C. Agon Time-series data mining ACM Computing Surveys 45 2012 1201 1234
7. E. Fuchs, T. Gruber, H. Pree, and B. Sick Temporal data mining using shape space representation of time series Neurocomputing 74 2010 379 393
8. A. Hyvärinen Fast and robust fixed-point algorithms for independent component analysis IEEE Transactions on Neural Networks 10 3 1999 626 634
9. F. Itakura Minimum prediction residual principle applied to speech recognition IEEE Transactions on acoustics, Speech, and Signal Process 23 1 1975 52 72
10. Johannesmeyer, M. C. (1999). Abnormal situation analysis using pattern recognition techniques and historical data. M.S. thesis, UCSB, Santa Barbara, CA.
11. I.T. Jolliffe Principal component analysis 2004 Springer New York
12. Karamitopoulos, L.; Evangelidis, G.; Dervos, D. (2008). Multivariate time series data mining: PCA-based measures for similarity search. In Proceedings of the 2008 international conference in data mining (pp. 253-259).
13. L. Karamitopoulos, and G. Evangelidis PCA-based time series similarity search Data Mining Annals of Information Systems 8 2010 255 276
14. Keogh, E.; Pazzani, M. (2001). Derivative dynamic time warping. In Proceedings of the first SIAM international conference on data mining (pp. 1-11).
15. E. Keogh, E. Chakrabarti, M. Pazzani, and S. Mehrotra Dimensionality reduction for fast similarity search in large time series databases Journal of Knowledge and Information Systems 3 2000 263 286
16. Keogh, E.; Chu, S.; Hart, D.; Pazzani, M. (2001). An online algorithm for segmenting time series. In Proceedings of the 2001 IEEE international conference on data mining (pp. 289-296).
17. W. Krzanowski Between-groups comparison of principal components Journal of the Acoustical Society of America 74 1979 703 707
18. A.J.T. Lee, H.W. Wu, and T.Y. Lee Data & Knowledge Engineering 68 2009 1071 1090
19. D. Lemire Faster retrieval with a two-pass dynamic-time-warping lower bound Pattern Recognition 42 2009 2169 2180
20. H. Li, and C. Guo Piecewise cloud approximation for time series mining Knowledge-Based Systems 24 2011 492 500
21. H. Li, C. Guo, and W. Qiu Similarity measure based on piecewise linear approximation and derivative dynamic time warping for time series mining Expert Systems with Applications 38 2011 14732 14743
22. C. Li, L. Khan, and B. Prabhakaran Real-time classification of variable length multi-attribute motion data International Journal of Knowledge and Information Systems 10 2 2006 163C183
23. Lin, J.; Keogh, E.; Lonardi, S.; Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the eighth ACM SIGMOD international conference on management of data workshop on research issues in data mining and knowledge discovery (pp. 2-11).
24. E.A. Maharaj, and P.D. Urso Fuzzy clustering of time series in the frequency domain Information Sciences 181 2011 1187 1211
25. Muscillo, R.; Conforto, S.; Schmid, S. (2007). Classification of motor activities through derivative dynamic time warping applied on accelerometer aata. In: Proceedings of the 29th annual international conference of engineering in medicine and biomedicine (pp. 4930-4933).
26. S.E. Papadakis, and V.G. Kaburlasos Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals numbers (INs) Information Sciences 180 2010 5060 5076
27. J.L. Rodgers, and W.A. Nicewander Thirteen ways to look at the correlation coefficient The American Statistician 42 1988 59 66
28. H. Sakoe, and S. Chiba Dynamic programming algorithm optimization for spoken word recognition IEEE Transactions on Acoustics, Speech, and Signal Process 26 1 1978 43 49
29. Sakurai, Y.; Yoshikawa, M.; Faloutsos, C. (2005). FTW: Fast similarity search under the time warping distance. In Proceedings of the 2005 principles of database systems (pp. 326-337).
30. S. Salvador, and P. Chan FastDTW: Toward accurate dynamic time warping in linear time and space Intelligent Data Analysis 11 2007 561 580
31. Shatkay, H.; Zdonik, S. (1996). Approximation queries and representations for large data sequences. In Proceedings of the 12th IEEE international conference on data engineering (pp. 536-545).
32. Singhal, D.; Seborg, A. (2002). Clustering of multivariate time-series data. In Proceedings of the American control conference (Vol. 5).
33. A. Singhal, and D.E. Seborg Clustering multivariate time-series data Journal of Chemometrics 19 2005 427 438
34. Spiegel, S.; Gaebler, J.; Lommatzsch, A.; et al. (2011). Pattern recognition and classification for multivariate time series. In Proceedings of the fifth international workshop on knowledge discovery from sensor data (pp. 1-9).
35. Struzik, R.; Siebes, A. (1998). Wavelet transform in similarity paradigm. In Proceedings of the second Pacific-Asia conference on knowledge discovery and data mining (pp. 295-309).
36. Struzik, R.; Siebes, A. (1999). The haar wavelet transform in the time series similarity paradigm. In Proceedings of the third European conference on principles and practice of knowledge discovery in databases (pp. 12-22).
37. H. Wang Block principal component analysis with L1-norm for image analysis Pattern Recognition Letters 33 2012 537 542
38. X. Weng, and J. Shen Classification of multivariate time series using two-dimensional singular value decomposition Knowledge-Based Systems 21 7 2008 535 539
39. Wong, T. S. F.; Wong, M. H. (2003). Efficient subsequence matching for sequences databases under time warping. In Proceedings of the seventh international database engineering and applications symposium (pp. 139-148).
40. E.C. Wu, and P.H. Yu Independent component analysis for clustering multivariate time series data Advanced Data Mining and Applications 2005 474 482
41. Yang, K.; Shahabi, C. (2004). A PCA-based similarity measure for multivariate time series. In Proceedings of the second ACM international workshop on multimedia databases (pp. 65-74).
42. D. Yu, X. Yu, and Q. Hu Information Sciences 181 2011 2787 2796
43. M. Zhou, and M.H. Wong A segment-wise time warping method for time scaling searching Information Sciences 173 1 2005 227 254