A novel two-level clustering method for time series data analysis

Expert Systems with Applications

Volumn 37, Issue 9, 2010, Pages 6319-6326

  Lai, Cheng Ping ^a,b   Chung, Pau Choo ^a   Tseng, Vincent S ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b NATIONAL CHENG KUNG UNIVERSITY HOSPITAL   (Taiwan)

Author keywords

Clustering; Data sampling; Dimension reduction; Time series analysis

Indexed keywords

APPROXIMATION ALGORITHMS; CLUSTERING ALGORITHMS; DATA REDUCTION; REDUCTION; TIME SERIES ANALYSIS;

CLUSTERING; DATA SAMPLING; DIMENSION REDUCTION; DIMENSION REDUCTION METHOD; EXPERIMENTAL EVALUATION; PIECEWISE AGGREGATE APPROXIMATION; TIME SERIES CLUSTERING; TIME SERIES DATA ANALYSIS;

CLUSTER ANALYSIS;

EID: 80051606484     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.02.089     Document Type: Article

References (25)

1. Banerjee, A., & Ghosh, J. (2001). Clickstream clustering using weighted longest common subsequences. In Workshop on web mining of the first SIAM international conference on data mining, Chicago, USA.
2. Ben-Dor, A., & Yakhini, Z. (1998). Clustering gene expression pattern. Technique report of Hewlett-Packard Laboratories Israel HPL-98-190.
3. Berndt, D. J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series (pp. 359-370). Working notes of the knowledge discovery in databases workshop, Washington, USA.
4. Dermatas, E., & Kokkinakis, G. (1996). Algorithm for clustering continuous density HMM by recognition error. IEEE Transactions on Speech and Audio Processing, 4(3), 231-234. (Pubitemid 126753009)
5. Duan, G., Suzuki, Y., & Kawagoe, K. (2006). Grid representation of time series data for similarity search. In Proceedings of the 22nd international conference on data engineering workshops (ICDEW'06) (p. x123).
6. Focardi, S. M. (2001). Clustering economic and financial time series: Exploring the existence of stable correlation conditions. Discussion paper 2001-04, The Intertek Group 94, rue de Javel F-75015, Paris.
7. Ghyselsy, E., & Valkanov, R. (2006). Linear time series processes with mixed data sampling and MIDAS regression models. Social science research network.
8. Guo, C., Jia, H., & Zhang, N. (2008). Time series clustering based on ICA for stock data analysis. In Proceedings of the fourth international conference wireless communications, networking and mobile computing, WiCOM '08, 12-14 October (pp. 1-4).
9. Hathaway, R. J., & Bezdek, J. C. (2003). Visual cluster validity for prototype generator clustering models. Pattern Recognition Letters, 24(9-10), 1563-1569. (Pubitemid 36225861)
10. Hautamaki, V., Nykanen, P., & Franti, P. (2008). Time-series clustering by approximate prototypes. In Proceedings of the 19th international conference pattern recognition, 8-11 December (pp. 1-4).
11. Kalpakis, K., Gada, D., & Puttagunta, V. (2001). Distance measures for effective clustering of ARIMA time-series. In Proceedings of the 2001 IEEE international conference on data mining, San Jose, CA, November (pp. 273-280).
12. Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. John Wiley & Sons.
13. Keogh, E., Lin, J., & Fu, A. (2005). HOT SAX: Efficiently finding the most unusual time series subsequence. In Proceedings of the fifth IEEE international conference on data mining, Houston, Texas, USA (pp. 226-233). (Pubitemid 47385697)
14. Keogh, E., Lin, J., & Truppel, W. (2003). Clustering of time series subsequences is meaningless: Implications for past and future research. In Proceedings of the third IEEE international conference on data mining, Melbourne, FL, USA (pp. 115- 122).
15. Keogh, E., Lonardi, S., & Chiu, W. (2002). Finding surprising patterns in a time series database in linear time and space. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, Edmonton, Alberta, Canada, July 23-26 (pp. 550-556).
16. Keogh, E., & Ratanamahatana, C. A. (2004). Exact indexing of dynamic time warping, knowledge and information systems. London: Springer. pp. 258-386.
17. Kornfield, E. M., Manmatha, R., & Allan, J. (2004). Text alignment with handwritten documents. In Proceedings of the first international workshop on document image analysis for libraries (DIAL'04) (pp. 195-209).
18. Kruskall, J. B., & Liberman, M. (1983). The symmetric time warping algorithm: From continuous to discrete. Time warps, string edits and macromolecules. Addison- Wesley.
19. 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 workshop on research issues in data mining and knowledge discovery.
20. Qian, J., DolledFilhart, M., Lin, J., Yu, H., & Beyond, M. G. (2001). Synexpression relationships: Local clustering of timeshifted and inverted gene expression profiles identifies new, biologically relevant interactions. Journal of Molecular Biology, 314, 1053-1066. (Pubitemid 34073069)
21. Shou, Y., Mmaoulis, M., & Cheung, D. W. (2005). Fast and exact warping of time series using adaptive segmental approximations. Machine Learning, 58, 231-267. (Pubitemid 40400639)
22. Tseng, V. S., & Kao, C. P. (2005). Efficiently mining gene expression data via a novel parameterless clustering method. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2(4), 355-365. (Pubitemid 43060409)
23. Tseng, V. S., & Kao, C. P. (2007). A novel similarity-based fuzzy clustering algorithm by integrating PCM and mountain method. IEEE Transactions on Fuzzy Systems, 15(6), 1188-1196. (Pubitemid 350268648)
24. Tseng, V. S., Wang, K. H., & Lee, C. I. (2003). A preprocessing method to deal with missing values by integrating clustering and regression techniques. Applied Artificial Intelligence, 17(5), 535-544.
25. Vlachos, M., Kollios, G. & Gunopulos, D. (2002). Discovering similar multidimensional trajectories. In Proceedings of the 18th international conference on data engineering, San Jose, CA, February 26 to March 1.