Matching and retrieving sequential patterns using regression

Web Intelligence and Agent Systems

Volumn 3, Issue 4, 2005, Pages 261-270

  Lei, Hansheng ^a   Govindaraju, Venu ^a  

^a UNIVERSITY AT BUFFALO   (United States)

Author keywords

Pruning; Regression; Rule discovery; Sequential pattern

Indexed keywords

DATABASE SYSTEMS; MARKETING; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

PRUNING; REGRESSION; RULE DISCOVERY; SEQUENTIAL PATTERN;

PATTERN MATCHING;

EID: 29244443711     PISSN: 15701263     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (18)

1. R. Agrawal, C. Faloutsos and A. Swami, Efficient similarity search in sequence, database. Proceedings of the 4th International Conference on Foundations of Data Organizations and Algorithms, 1993, 69-84.
2. R. Agrawal, K.I. Lin, H. Sawhney and K. Shim, Fast similarity search in the presence of noise, scaling, and translation in time-series databases, Proceedings of the 21st International Conference on Very Large Databases, 1995, 490-501.
3. D. Berndt and J. Clifford, Using dynamic time warping to find patterns in time series, Working Notes of the Knowledge Discovery in Databases Workshop, 1994, 359-370.
4. K. Chu and M. Wong, Fast time-series searching with scaling and shifting, The 18th ACM Symp. on Principles of Database Systems, 1999, 237-248.
5. C. Faloutsos, M. Ranganathan and Y. Manolopoulos, Fast subsequence matching in time-series databases (1994), 419-429.
6. D.Q. Goldin and P.C. Kanellakis, On similarity queries for time-series data: Constraint specification and implementation, The 1st International Conference on the Principles and Practice of Constraint Prog, 1995, 137-153.
7. A. Jain, F. Griess and S. Connell, On-line signature verification, Pattern Recognition 35 (2002)(12), 2963-2972.
8. E. Keogh, Exact indexing of dynamic time warping, Proceedings of the 28th International Conference on Very Large Data Bases, 2002, 406-417.
9. S. Kim and S. Park, An index-based approach for similarity search supporting time warping in large sequence databases, Proceedings of the 17th International Conference on Data Engineering, 2001, 607-614.
10. H. Lei and V. Govindaraju, GRM: Generalized regression model for clustering linear sequences, Proceedings of the 4th SIAM International Conference on Data Mining, 2004.
11. F. Mosteller and J. Tukey, Data analysis and regression: A second, course in statistics, Addison-Wesley, 1977.
12. V. Nalwa, Automatic on-line signature verification, Proceedings of the IEEE 85 (1997)(2), 213-239.
13. S. Park, W. Chu, J. Yoon and C. Hsu, An index-based approach for similarity search supporting time warping in large sequence databases, Proceedings of the 16th International Conference on Data Engineering, 2000, 23-32.
14. D. Rafiei and A. Mendelzon, Efficient retrieval of similar time sequences using dft, Proceedings of the 5th International Conference on Foundations of Data Organizations and Algorithms (FODO), 1998, 69-84.
15. T. Bozkaya, N. Yazdani and Z. Ozsoyoglu, Matching and indexing sequences of different lengths, 1997, 128-135.
16. J. Wooldridge, Introductory econometrics: A modern approach, (second ed.), South-Western College Publishing, 1999.
17. B. Yi, H. Jagadish and C. Faloutsos, Efficient retrieval of similar time sequences under time warping, Proceedings of the 14th International Conference on Data Engineering, 1998, 23-27.
18. Y. Zhu and D. Shaha, Statstream: Statistical monitoring of thousands of data streams in real time, Proceedings of the 28th International Conference on Very Large Data Bases, 2002.