(Not) finding rules in time series: A surprising result with implications for previous and future research

Proceedings of the International Conference on Artificial Intelligence IC-AI 2003

Volumn 1, Issue , 2003, Pages 55-61

  Lin, Jessica ^a   Keogh, Eamonn ^a   Truppel, Wagner ^a  

^a University of California Riverside ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATA STRUCTURES; FINANCE; MARKETING; RANDOM PROCESSES;

CLUSTERING; TIME SERIES DATA; TIME SERIES RULE DISCOVERY ALGORITHMS;

DATA MINING;

EID: 1642272852     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (38)

1. Agrawal, R., Imielinski, T. & Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. In proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. Washington, D.C., May 26-28, pp. 207-216.
2. Bar-Joseph, Z., Gerber, G., Gifford, D., Jaakkola, T. & Simon, I. (2002). A New Approach to Analyzing Gene Expression Time Series Data. In proceedings of the 6th Annual Int'l Conference on Research in Computational Molecular Biology. Washington, D.C., Apr 18-21. pp 39-48.
3. Bradley, P. S. & Fayyad, U.M. (1998). Refining Initial Points for K-Means Clustering. In proceedings of the 15th Int'l Conference on Machine Learning. Madison, WI, July 24-27. pp.91-99.
4. British Iris Society, Species Group Staff. (1997). A Guide to Species Irises: Their Identification and Cultivation. Cambridge University Press. March, 1997.
5. Cotofrei, P. (2002). Statistical Temporal Rules. In proceedings of the 15th Conference on Computational Statistics - Short Communications and Posters. Berlin, Germany, Aug 24-28.
6. Cotofrei, P. & Stoffel, K (2002). Classification Rules + Time = Temporal Rules. In proceedings of the 2002 Int'l Conference on Computational Science. Amsterdam, Netherlands, Apr 21-24, pp 572-581.
7. Das, G., Lin, K., Mannila, H., Renganathan, G. & Smyth, P. (1998). Rule Discovery from Time Series. In proceedings of the 4th Int'l Conference on Knowledge Discovery and Data Mining. New York, NY, Aug 27-31. pp 16-22.
8. Fisher, R. A. (1936). The Use of Multiple Measures in Taxonomic Problems. Annals of Eugenics. Vol. 7, No. 2, pp 179-188.
9. Fu, T. C., Chung, F. L., Ng, V. & Luk, R. (2001). Pattern Discovery from Stock Time Series Using Self-Organizing Maps. Workshop Notes of KDD2001 Workshop on Temporal Data Mining. San Francisco, CA, Aug 26-29. pp. 27-37.
10. Gavrilov, M., Anguelov, D., Indyk, P. & Motwani, R. (2000). Mining the Stock Market: Which Measure is Best? In proceedings of the 6th ACM Int'l Conference on Knowledge Discovery and Data Mining. Boston, MA, Aug 20-23. pp 487-496.
11. Halkidi, M., Batistakis, Y. & Vazirgiannis, M. (2001). On Clustering Validation Techniques. Journal of Intelligent Information Systems (JIIS), Vol. 17, No. 2-3. pp. 107-145.
12. Harms, S. K., Deogun, J. & Tadesse, T. (2002). Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences. In proceedings of the 13th Int'l Symposium on Methodologies for Intelligent Systems. Lyon, France, June 27-29, pp 432-441.
13. Harms, S. K., Reichenbach, S. Goddard, S. E., Tadesse, T. & Waltman, W. J. (2002). Data Mining in a Geospatial Decision Support system for Drought Risk Management. In proceedings of the 1st National Conference on Digital Government. May 21-23. pp. 9-16.
14. Hetland, M. L. & Sætrom, P. (2002). Temporal Rules Discovery Using Genetic Programming and Specialized Hardware. In proceedings of the 4th Int'l Conference on Recent Advances in Soft Computing.
15. Honda, R., Wang, S., Kikuchi, T. & Konishi, O. (2002). Mining of Moving Objects from Time-Series Images and its Application to Satellite Weather Imagery. The Journal of Intelligent Information Systems, Vol. 19, No. 1.
16. Jensen, D. (2000). Data Snooping, Dredging and Fishing: The dark Side of Data Mining. SIGKDD99 panel report. ACM SIGKDD Explorations, Vol. 1, No. 2.
17. Jin, X., Lu, Y. & Shi, C. (2002). Distribution Discovery: Local Analysis of Temporal Rules. In proceedings of the 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Taiwan, May 6-8. pp 469-480.
18. Jin, X., Wang, L., Lu, Y. & Shi, C. (2002). Indexing and Mining of the Local Patterns in Sequence Database. In proceedings of the 3rd International Conference on Intelligent Data Engineering and Automated Learning. Manchester, UK, Aug 12-14. pp. 68-73.
19. Keogh, E. (2002). Exact Indexing of Dynamic Time Warping. In proceedings of the 28th International Conference on Very Large Data Bases. Hong Kong, Aug 20-23. pp 406-417.
20. Keogh, E. Chakrabarti, K. Pazzani, M & Mehrotra, S. (2001). Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Journal of Knowledge and Information Systems. Vol. 3, No. 3, pp. 263-286.
21. Keogh, E. & Kasetty, S. (2002). On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. July 23 - 26, 2002. Edmonton, Alberta, Canada. pp 102-111.
22. Li, C., Yu, P. S. & Castelli, V. (1998). MALM: A Framework for Mining Sequence Database at Multiple Abstraction Levels. In proceedings of the 7th ACM CIKM Int'l Conference on Information and Knowledge Management. Bethesda, MD, Nov 3-7. pp 267-272.
23. Lin, J. Keogh, E. Patel, P & Lonardi, S. (2002). Finding motifs in time series. In the 2nd Workshop on Temporal Data Mining, at the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. July 23 - 26, 2002. Edmonton, Alberta, Canada.
24. Mantegna, R. N. (1999). Hierarchical Structure in Financial Markets. European. Physical Journal. B11, pp. 193-197.
25. Mori, T. & Uehara, K. (2001). Extraction of Primitive Motion and Discovery of Association Rules from Human Motion. In proceedings of the 10th IEEE Int'l Workshop on Robot and Human Communication, Sept 18-21. pp 200-206.
26. Oates, T. (1999). Identifying Distinctive Subsequences in Multivariate Time Series by Clustering. In proceedings of the 5th International Conference on Knowledge Discovery and Data Mining. Aug 15-18. pp 322-326.
27. Osaki, R., Shimada, M. & Uehara, K. (2000). A Motion Recognition Method by Using Primitive Motions, Arisawa, H. and Catarci, T. (eds.) Advances in Visual Information Management, Visual Database Systems. pp 117-127.
28. Radhakrishnan, N., Wilson, J. D. & Loizou, P. C. (2000). An Alternate Partitioning Technique to Quantify the Regularity of Complex Time Series. International Journal of Bifurcation and Chaos, Vol. 10, No. 7. World Scientific Publishing. pp 1773-1779.
29. Roddick, J. F. & Spiliopoulou, M. (2002). A Survey of Temporal Knowledge Discovery Paradigms and Methods. Transactions on Data Engineering. Vol. 14, No. 4, pp. 750-767.
30. Sarker, B. K., Mori, T. & Uehara, K. (2002). Parallel Algorithms for Mining Association Rules in Time Series Data. CS24-2002-1 Tech report.
31. Schittenkopf, C., Tinio, P. & Dorffner, G. (2000). The Benefit of Information Reduction for Trading Strategies. Report Series for Adaptive Information Systems and Management in Economics and Management Science.
32. Steinback, M., Tan, P.N., Kumar, V., Klooster, S. & Potter, C. (2002). Temporal Data Mining for the Discovery and Analysis of Ocean Climate Indices. In the 2nd Workshop on Temporal Data Mining, at the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada. July 23.
33. Timmermann, A., Sullivan, R. & White, H. (1998). The Dangers of Data-Driven Inference: The Case of Calendar Effects in Stock Returns. FMG Discussion Papers dp0304, Financial Markets Group and ESRC.
34. Tino, P., Schittenkopf, C. & Dorffner, G. (2000). Temporal Pattern Recognition in Noisy Non-stationary Time Series Based on Quantization into Symbolic Streams: Lessons Learned from Financial Volatility Trading. Report Series for Adaptive Information Systems and Management in Economics and Management Science, July. Report #46.
35. Truppel, Keogh, Lin (2003). A Hidden Constraint When Clustering Streaming Time Series. UCR Tech Report.
36. Uehara, K. & Shimada, M. (2002). Extraction of Primitive Motion and Discovery of Association Rules from Human Motion Data. Progress in Discovery Science 2002, Lecture Notes in Artificial Intelligence, Vol. 2281. Springer-Verlag. pp 338-348.
37. Walker, J. (2001). HotBits: Genuine Random Numbers Generated by Radioactive Decay. www.fourmilab.ch/hobits/
38. Yairi, T., Kato, Y. & Hori, K. (2001). Fault Detection by Mining Association Rules in House-keeping Data. In proceedings of the 6th International Symposium on Artificial Intelligence, Robotics and Automation in Space.