Efficient similarity matching for categorical sequence based on dynamic partition

Proceedings of the Eigtht IASTED International Conference on Software Engineering and Applications

Volumn , Issue , 2004, Pages 13-18

  Cai, Keke ^a   Chen, Chun ^a   Lin, Huaizhong ^b  

^a ZHEJIANG UNIVERSITY   (China)

^b Zhejiang University ^*

Author keywords

Categorical sequence; Dynamic partition and longest common subsequences; Sequence matching; Time series

Indexed keywords

ALGORITHMS; DATA ACQUISITION; DATABASE SYSTEMS; DISCRETE FOURIER TRANSFORMS; MATHEMATICAL MODELS; SPEECH RECOGNITION; TIME SERIES ANALYSIS;

CATEGORICAL SEQUENCE; DYNAMIC PARTITION; LONGEST COMMON SUBSEQUENCES; SEQUENCE MATCHING;

DATA MINING;

EID: 11144233255     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. R. Agrawal, C. Faloutsos, and A. Swami. Efficient Similarity Search in Sequence Databases. In Proc. 4th Int'l Conf. on Foundations of Data Organization and Algorithms, 1993, 69-84.
2. Y. Saygin, O. Ulusoy, Exploiting Data Mining Techniques for Broadcasting Data in Mobile Computing Environments. IEEE Transactions on Knowledge and Data Engineering, 14(6), November/December, 2002.
3. R. Agrawal, K. Lin, H. S. Sawhney and K. Shim, Fast similarity search in the presence of noise, scaling, and translation in time-series databases. Proc. Int. Conf. Very Large Data Bases (VLDB), 1995, 490-501.
4. A.Guttman, R-tree: a Dynamic Index Structure for Spatial Searching. Proc. Of ACM SIGMOD Conf. On Management of Data, 1984, 45-57.
5. A.Oppenheim and R. Schafer, Digital signal processing (Prentice-Hall, 1975).
6. R. Edwards and J. Magee, Teehnical analysis of stock trends (Springfield, Massachsetts, 1969).
7. J.S.Varre,J.P.Delahaye, E.Rivals,The Transformation Distance: A Dissimilarity Measure Based on Movements of Segments, Bioinformatics, 15(4),1999, 194-202.
8. K. K. W. Chu and M. H. Wong, Fast time-series searching with scaling and shifting. In PODS, 1999, 237-248.
9. V. I. Levenshtein, Binary codes capable of correction deletions, insertions and reversals, Cybernetics and Control Theory, 10(8), 1996, 707-710.
10. D.Rafiei and A.Mendelzon. Similarity-based Queries for Time Series Data. In Proc. Of the ACM SIGMOD Intl. Conf. On Mgmt. Of Data(SIGMOD'97), Tucson, Arizona, May 1997, 13-24.
11. Gautam Das, Heikki Mannila, Pirjo Ronkainen: Similarity of Attributes by External Probes. KDD, 1998, 23-29.
12. Banerjee A, Ghosh J. Clickstram, Clustering Using Weighted Longest Common Subsequences. In Proc. Of Workshop on Web Mining in First International SIAM Conference on Data Mining, Chicago, 2001, 33-40.
13. J. Yoon, Y. Luo and J. Nam, A Bitmap Approach to Trend Clustering and Prediction in Time-Series Databases. In Proc. of Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, Florida, April 2001.
14. A.Guttman. R-Trees: A Dynamic Index Structure for Spatial Searching. SIGMOD Conference, 1984, 47-57.