Clustering time series using unsupervised-shapelets

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2012, Pages 785-794

  Zakaria, Jesin ^a   Mueen, Abdullah ^a   Keogh, Eamonn ^a  

^a University of California Riverside   (United States)

Author keywords

Clustering; Shapelets; Time series; Unsupervised

Indexed keywords

CLUSTERING; CLUSTERING RESULTS; CLUSTERING TIME SERIES; DISTANCE MEASURE; DIVERSE DOMAINS; DYNAMIC TIME WARPING DISTANCE; EMPIRICAL EVALUATIONS; EUCLIDEAN DISTANCE; GAIT CYCLES; HEART BEATS; LOCAL PATTERNS; REAL-WORLD PROBLEM; RESEARCH TOPICS; SHAPELETS; TIME SERIES CLUSTERING; TIME SERIES DATA MINING; UNSUPERVISED;

CLUSTERING ALGORITHMS; DATA MINING;

TIME SERIES;

EID: 84874084961     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2012.26     Document Type: Conference Paper

References (34)

1. D. J. Berndt and J. Clifford, "Using Dynamic Time Warping to find Patterns in Time Series," AAAI-94 Workshop on Knowledge Discovery in Databases, pp. 229-248.
2. D. Cerra, J. Bieniarz, J. Avbelj, P. Reinartz, and R. Mueller, "Compression-based unsupervised clustering of spectral signatures," Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2011).
3. H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, "Querying and mining of time series data: experimental comparison of representations and distance measures," PVLDB 2008, pp 1542-52.
4. M. Garilov, D. Anguelov, P. Indyk, and R. Motwani, "Mining the stock market: Which measure is best?" Proc. ACM KDD 2000.
5. A. L. Goldberger, et al. PhysioBank, PhysioToolkit, and PhysioNet: Circulation. Discovery vol. 101 (1997).
6. M. Halkidi, Y. Batistakis, and M. Vazirgiannis, "On clustering validation techniques," Journal of Intelligent Information Systems.
7. B. Hartmann, I. Schwab, and N. Link, "Prototype optimization for temporarily and spatially distorted Time Series," AAAI Spring Symposium Series (SSS 2010), pp. 15-20.
8. S. Hirano and S. Tsumoto, "Cluster analysis of Time-series Medical data based on the Trajectory representation and multiscale comparison techniques," Proc. ICDM 2006.
9. D. Jiang, C. Tang, and A. Zhang, "Cluster analysis for gene expression data: A survey," IEEE Trans. On Knowledge and data engineering (2004), vol. 16, no. 11, pp. 1370-1386.
10. K. Kalpakis, D. Gada, and V. Puttagunta, "Distance measures for effective clustering of ARIMA Time-Series," ICDM 2001.
11. E. Keogh, S. Kasetty "On the need for Time Series Data Mining Benchmarks: a survey and empirical demonstration," Proc. ACM KDD 2002, pp. 102-111.
12. E. Keogh, J. Lin and W. Truppel, "Clustering of Time Series Subsequences is meaningless: implications for past and future research," Proc. IEEE ICDM 2003, pp. 115-122.
13. E. Keogh, Q. Zhu, B. Hu, Y. Hao, X. Xi, L. Wei, and C. A. Ratanamahatana, The UCR Time Series Classification/Clustering Homepage: www.cs.ucr.edu/~eamonn/ time-series-data/
14. T.W. Liao, "Clustering of time series data-a survey," Pattern Recognition, vol. 38, 2005, pp. 1857-1874.
15. J. Lines, A. Bagnall, P. C. Smith, and S. Anderson, "Classification of Household Devices by Electricity Usage Profiles," IDEAL 2011, LNCS vol. 6936.
16. J. Lines, L. Davis, J. Hills, and A. Bagnall, "A Shapelet filter for time series classification," ACM SIGKDD 2012, in press.
17. J. Lin, R. Khade, and Y. Li, 2012. "Rotation-invariant similarity in time series using Bag-of-Patterns representation," Journal of Intelligent Information Systems, 2012, in press.
18. R. Kosala and H. Blockeel, "Web Mining Research: A Survey," ACM SIGKDD, 2000.
19. F. Möerchen, "Time series feature extraction for data mining using dwt and dft," Technical Report No. 33, 2003.
20. K. Muata, "Post-pruning in decision tree induction using multiple performance measures," Computers and Operations Research, 2007.
21. A. Mueen, E. Keogh, and N. Young, "Logical-Shapelets: an expressive primitive for Time Series classification," Proc. ACM SIGKDD, 2011, pp. 1154-1162.
22. A. Mueen, S. Nath, and J. Liu, "Fast approximate correlation for massive Time-Series data," SIGMOD 2010, pp. 171-182.
23. T. Rakthanmanon, E. Keogh, S. Lonardi, and S. Evans, "Time Series Epenthesis: Clustering Time Series Streams requires ignoring some Data," Proc. IEEE ICDM 2011.
24. W. M. Rand, "Objective criteria for the evaluation of clustering methods," Journal of the American Statistical Association (1971).
25. C. A. Ratanamahatna and E. Keogh, "Making Time-series classification more accurate using learned constraints," Proc. SIAM International Conference on Data Mining (SDM 2004).
26. E. J. Ruiz, V. Hristidis, C. Castillo, and A. Gionis, "Correlating Financial Time Series with Micro-Blogging activity," WSDM 2012.
27. G. Salton, A. Wong, and C. S. Yang, "A vector space model for automatic indexing," Commun. ACM (1975) vol. 19, pp. 613-620.
28. S. Shariat and V. Pavlovic, "Isotonic CCA for sequence alignment and activity recognition," Proc. IEEE International Conference on Computer Vision (ICCV 2011), pp. 2572-2578.
29. X. Wang, K. Smith, and R. Hyndman, "Characteristic-based clustering for time series data," DMKD, vol. 13, 2006, pp. 335-364.
30. Webpage of our paper: https://sites.google.com/site/icdmclusteringts/, password: ICDM2012.
31. Z. Xing, J. Pei, P. Yu, and K. Wang, "Extracting interpretable features for early classification on time series," Proc. SDM 2011.
32. L. Ye and E. Keogh, "Time Series Shapelets: a new primitive for Data Mining," Proc. ACM SIGKDD, 2009, pp. 947-956.
33. H. Zhang, T. B. Ho, Y. Zhang, and M. S. Lin, "Unsupervised Feature Extraction for Time Series Clustering using Orthogonal Wavelet Transform," Journal on Informatics, vol. 30, Sep 2005, pp. 305-319.
34. M. Zhang and A. A. Sawchuk, "Motion Primitive-Based Human Activity Recognition Using a Bag-of-Features Approach," ACM SIGHIT International Health Informatics Symp. (IHI 2012), pp. 1-10.