Mining motifs in massive time series databases

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2002, Pages 370-377

  Patel, Pranav ^a   Keogh, Eamonn ^a   Lin, Jessica ^a   Lonardi, Stefano ^a  

^a University of California Riverside   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 2442567436     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (40)

1. th Int'l Conference on Foundations of Data Organization and Algorithms. Chicago, IL, Oct 13-15. pp 69-84.
2. st Int'l Conference on Very Large Databases. Zurich, Switzerland, Sept 11-15. pp 502-514.
3. st European Symposium. Trondheim, Norway, Jun 24-27. pp 211-220.
4. th Int'l Conference on Research in Computational Molecular Biology. Washington, DC, April 18-21. pp 22-31.
5. Burkhard, W. A. & Keller, R. M. (1973). Some approaches to best-match file searching. Commun. ACM, April. Vol. 16(4), pp 230-236.
6. Böhm, C., Braunmüller, B., Krebs, F. & Kriegel, H. P. (2002). Epsilon grid order: An algorithm for the similarity join on massive high-dimensional data. In proceedings of ACM SIGMOD Int. Conf. on Management of Data, Santa Barbara.
7. th Int'l Conference on Knowledge Discovery and Data Mining. New York, NY, Aug 27-31. pp 9-15.
8. th IEEE Int'l Conference on Data Engineering. Sydney, Australia, Mar 23-26. pp 126-133.
9. th Int'l Conference on Knowledge Discovery and Data Mining. New York, NY, Aug 27-31. pp 16-22.
10. Daw, C. S., Finney, C. E. A. & Tracy, E. R. (2001). Symbolic analysis of experimental data. Review of Scientific Instruments. To appear.
11. Durbin, R., Eddy, S., Krogh, A. & Mitchison, G. (1998). Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press.
12. th International Conference on Knowledge Discovery and Data Mining. New York, NY, Aug 27-31. pp 194-198.
13. th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, MA, Aug 20-23. pp 81-90.
14. Goutte, C. Toft, P., Rostrup, E.,. Nielsen F.Å & Hansen L.K. (1999). On clustering fMRI time series, NeuroImage, 9(3): pp 298-310.
15. Hegland, M., Clarke, W. & Kahn, M. (2002). Mining the MACHO dataset, Computer Physics Communications, Vol 142(1-3), December 15. pp. 22-28.
16. Hertz, G. & Stormo, G. (1999). Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics, Vol. 15, pp 563-577.
17. van Helden, J., Andre, B., & Collado-Vides, J. (1998) Extracting regulatory sites from the upstream region of the yeast genes by computational analysis of oligonucleotides. J. Mol. Biol., Vol. 281, pp 827-842.
18. th European Conference on Principles and Practice of Knowledge Discovery in Databases. Freiburg, Germany, pp 192-203.
19. th Int'l Conference on Knowledge Discovery and Data Mining. San Diego, CA, Aug 15-18. pp 282-286.
20. th Int'l Conference on Very Large Data Bases. Cairo, Egypt, Sept 10-14. pp 363-372.
21. 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, Nov 29-Dec 2. pp 273-280.
22. Keogh, E,. Chakrabarti, K,. Pazzani, M. & Mehrotra (2000). Dimensionality reduction for fast similarity search in large time series databases. Journal of Knowledge and Information Systems. pp 263-286.
23. Keogh, E., Chakrabarti, K., Pazzani, M. & Mehrotra, S. (2001). Locally adaptive dimensionality reduction for indexing large time series databases. In proceedings of ACM SIGMOD Conference on Management of Data. Santa Barbara, CA, May 21-24. pp 151-162.
24. th Int'l Conference on Knowledge Discovery and Data Mining. New York, NY, Aug 27-31. pp 239-241.
25. Koski, A., Juhola, M. & Meriste, M. (1995). Syntactic recognition of ECG signals by attributed finite automata. Pattern Recognition, 28 (12), pp. 1927-1940.
26. Lawrence, C.E., Altschul, S. F., Boguski, M. S., Liu, J. S., Neuwald, A. F. & Wootton, J. C. (1993). Detecting subtle sequence signals: A Gibbs sampling strategy for multiple alignment. Science, Oct. Vol. 262, pp 208-214.
27. th ACM CIKM International Conference on Information and Knowledge Management. Bethesda, MD. pp 267-272.
28. Lonardi, S. (2001). Global Detectors of Unusual Words: Design, Implementation, and Applications to Pattern Discovery in Biosequences. PhD thesis, Department of Computer Sciences, Purdue University, August, 2001.
29. th International Conference on Data Engineering.
30. th International Conference on Intelligent Systems for Molecular Biology. La Jolla, CA, Aug 19-23. pp 269-278.
31. Reinert, G., Schbath, S. & Waterman, M. S. (2000). Probabilistic and statistical properties of words: An overview. J. Comput. Bio., Vol. 7, pp 1-46.
32. Roddick, J. F., Hornsby, K. & Spiliopoulou, M. (2001). An Updated Bibliography of Temporal, Spatial and Spatio-Temporal Data Mining Research. In Post-Workshop Proceedings of the International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining. Berlin, Springer. Lecture Notes in Artificial Intelligence. 2007. Roddick, J. F. and Hornsby, K., Eds. 147-163.
33. Shasha, D. & Wang, T. (1990). New techniques for best-match retrieval. ACM Trans. on Information Systems, Vol. 8(2). pp 140-158. (Pubitemid 21681506)
34. Staden, R. (1989). Methods for discovering novel motifs in nucleic acid sequences. Comput. Appl. Biosci., Vol. 5(5). pp 293-298.
35. th International Workshop on Database & Expert Systems Applications. pp 162-166.
36. th Int'l Conference on Computational Molecular Biology. Montreal, Canada, Apr 22-25. pp 67-74.
37. th International Conference on Data Engineering. pp 673-684.
38. st IEEE International Conference on Data Mining. pp. 550-557.
39. Yang, J., Yu, P., Wang, W. and Han, J. (2002). Mining long sequential patterns in a noisy environment. In proceedings SIGMOD International, Conference on Management of Data. Madison, WI. (Pubitemid 34985565)
40. st Intl Conference on Very Large Databases. pp 385-394.