Time-series classification in many intrinsic dimensions

Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010

Volumn , Issue , 2010, Pages 677-688

  Radovanović, Milos ^a   Nanopoulos, Alexandros ^b   Ivanović, Mirjana ^a  

^a UNIVERSITY OF NOVI SAD   (Serbia)

^b UNIVERSITY OF HILDESHEIM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); NEAREST NEIGHBOR SEARCH; TEXT PROCESSING; TIME SERIES;

CURSE OF DIMENSIONALITY; DYNAMIC TIME WARPING; EMPIRICAL MEASUREMENT; EXPERIMENTAL EVALUATION; INTRINSIC DIMENSIONALITIES; INTRINSIC DIMENSIONS; K-NN CLASSIFICATIONS; TIME SERIES CLASSIFICATIONS;

DATA MINING;

EID: 84879888269     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972801.59     Document Type: Conference Paper

References (38)

1. C. C. Aggarwal and P. S. Yu. Outlier detection for high dimensional data. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 37-46, 2001.
2. J.-J. Aucouturier and F. Pachet. A scale-free distribution of false positives for a large class of audio similarity measures. Pattern Recognition, 41(1):272-284, 2007.
3. R. E. Bellman. Adaptive Control Processes: A Guided Tour. Princeton University Press, 1961.
4. D. J. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In Proc. AAAI Workshop on Knowledge Discovery in Databases (KDD), pages 359-370, 1994.
5. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
6. K. Chakrabarti, E. Keogh, S. Mehrotra, and M. Pazzani. Locally adaptive dimensionality reduction for indexing large time series databases. ACM Transactions on Database Systems, 27(2):188-228, 2002.
7. K.-P. Chan and W.-C. Fu. Efficient time series matching by wavelets. In Proc. IEEE Int. Conf. on Data Engineering (ICDE), pages 126-134, 1999.
8. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. The MIT Press, 2006.
9. L. Chen and R. Ng. On the marriage of lp-norms and edit distance. In Proc. 30th Int. Conf. on Very Large Data Bases (VLDB), pages 792-803, 2004.
10. L. Chen, M. T. Özsu, and V. Oria. Robust and fast similarity search for moving object trajectories. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 491-502, 2005.
11. H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. J. Keogh. Querying and mining of time series data: Experimental comparison of representations and distance measures. In Proc. 34th Int. Conf. on Very Large Data Bases (VLDB), pages 1542-1552, 2008.
12. G. Doddington, W. Liggett, A. Martin, M. Przybocki, and D. Reynolds. SHEEP, GOATS, LAMBS and WOLVES: A statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation. In Proc. Int. Conf. on Spoken Language Processing, 1998. Paper 0608.
13. D. Eads, D. Hill, S. Davis, S. Perkins, J. Ma, R. Porter, and J. Theiler. Genetic algorithms and support vector machines for time series classification. In Proc. of the International Society for Optical Engineering (SPIE), volume 4787, pages 74-85, 2002.
14. C. Faloutsos, M. Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 419-429, 1994.
15. D. Francois, V. Wertz, and M. Verleysen. The concentration of fractional distances. IEEE Transactions on Knowledge and Data Engineering, 19(7):873-886, 2007.
16. A. W.-C. Fu, E. J. Keogh, L. Y. H. Lau, C. A. Ratanamahatana, and R. C.-W. Wong. Scaling and time warping in time series querying. VLDB Journal, 17(4):899-921, 2008.
17. L. Gupta, D. L. Molfese, R. Tammana, and P. Simos. Nonlinear alignment and averaging for estimating the evoked potential. IEEE Transactions on Biomedical Engineering, 43(4):348-356, 1996.
18. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2nd edition, 2009.
19. A. Hicklin, C. Watson, and B. Ulery. The myth of goats: How many people have fingerprints that are hard to match? Technical report, National Institute of Standards and Technology, 2005.
20. A. Hinneburg, C. C. Aggarwal, and D. A. Keim. What is the nearest neighbor in high dimensional spaces? In Proc. Int. Conf. on Very Large Data Bases (VLDB), pages 506-515, 2000.
21. K. T.-U. Islam, K. Hasan, Y.-K. Lee, and S. Lee. Enhanced 1-NN time series classification using badness of records. In Proc. 2nd Int. Conf. on Ubiquitous Information Management and Communication, pages 108-113, 2008.
22. E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra. Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems, 3(3):263-286, 2001.
23. E. Keogh, C. Shelton, and F. Moerchen. Workshop and challenge on time series classification. Int. Conf. on Knowledge Discovery and Data Mining (KDD), 2007. http://www.cs.ucr.edu/~eamonn/SIGKDD2007Time-Series.html.
24. E. Keogh, X. Xi, L. Wei, and C. A. Ratanamahatana. The ucr time series classification/clustering homepage: www.cs.ucr.edu/~eamonn/time-series-data/, 2006.
25. F. Korn, B.-U. Pagel, and C. Faloutsos. On the "dimensionality curse" and the "self-similariy blessing,". IEEE Transactions on Knowledge and Data Engineering, 13(1):96-111, 2001.
26. E. Levina and P. J. Bickel. Maximum likelihood estimation of intrinsic dimension. In Advances in Neural Information Processing Systems 17, pages 777-784, 2005.
27. J. Lin, E. Keogh, L. Wei, and S. Lonardi. Experiencing SAX: A novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2):107-144, 2007.
28. C. Nadeau and Y. Bengio. Inference for the generalization error. Machine Learning, 52(3):239-281, 2003.
29. V. Niennattrakul and C. A. Ratanamahatana. Inaccuracies of shape averaging method using dynamic time warping for time series data. In Proc. 7th Int. Conf. on Computational Science (ICCS), LNCS 4487, pages 513-520, 2007.
30. V. Niennattrakul and C. A. Ratanamahatana. Shape averaging under time warping. In Proc. 6th Int. Conf. on Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology (ECTI-CON), 2009.
31. V. Pavlovic, B. J. Frey, and T. S. Huang. Time-series classification using mixed-state dynamic bayesian networks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 2, pages 2609-2615, 1999.
32. V. Petridis and A. Kehagias. Predictive modular neural networks for time series classification. Neural Networks, 10(1):31-49, 1997.
33. M. Radovanović, A. Nanopoulos, and M. Ivanović. Nearest neighbors in high-dimensional data: The emergence and influence of hubs. In Proc. Int. Conf. on Machine Learning (ICML), pages 865-872, 2009.
34. S. Salvador and P. Chan. Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms. In Proc. 16th IEEE Int. Conf. on Tools with Artificial Intelligence (ICTAI), pages 576-584, 2004.
35. G. A. F. Seber. Multivariate Observations. Wiley, 1984.
36. P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison Wesley, 2005.
37. M. Verleysen and D. François. The curse of dimensionality in data mining and time series prediction. In Proc. 8th Int. Workshop on Artificial Neural Networks (IWANN), LNCS 3512, pages 758-770, 2005.
38. M. Vlachos, D. Gunopoulos, and G. Kollios. Discovering similar multidimensional trajectories. In Proc. 18th Int. Conf. on Data Engineering (ICDE), page 0673, 2002.