Multiscale comparison of temporal patterns in time-series medical databases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2431 LNAI, Issue , 2002, Pages 188-199

  Hirano, Shoji ^a   Tsumoto, Shusaku ^a  

^a SHIMANE UNIVERSITY   (Japan)

Author keywords

Medical data mining; Multiscale matching; Rough clustering; Rough sets; Temporal knowledge discovery

Indexed keywords

DATA MINING; MEDICAL EDUCATION; ROUGH SET THEORY; TIME SERIES;

DISSIMILARITY MEASURES; INDEPENDENT CLUSTERS; MEDICAL DATA MINING; MULTISCALE COMPARISONS; MULTISCALE MATCHING; PHASE CONSTRAINTS; ROUGH CLUSTERING; TIME-SERIES MEDICAL DATABASE; HEPATITIS DATASET;

MEDICAL COMPUTING;

EID: 84864838678     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45681-3_16     Document Type: Conference Paper

References (18)

1. N. Ueda and S. Suzuki (1990): A Matching Algorithm of Deformed Planar Curves Using Multiscale Convex/Concave Structures. IEICE Transactions on Information and Systems, J73-D-II(7): 992-1000. 189, 191, 193
2. S. Hirano and S. Tsumoto (2001): Indiscernibility Degrees of Objects for Evaluating Simplicity of Knowledge in the Clustering Procedure. Proceedings of the 2001 IEEE International Conference on Data Mining. 211-217. 189
3. Z. Pawlak (1991): Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht. 189
4. R Agrawal, C. Faloutsos, and A. N. Swami (1993): Efficient Similarity Search in Sequence Databases. Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms: 69-84. 189
5. K. P. Chan and A. W. Fu (1999): Efficient Time Series Matching by Wavelets. Proceedings of the 15th IEEE International Conference on Data Engineering: 126-133. 189
6. F. Korn, H. V. Jagadish, and C. Faloutsos (1997): Efficiently Supporting Ad Hoc Queries in Large Datasets of Time Sequences. Proceedings of ACM SIGMOD International Conference on Management of Data: 289-300. 189
7. Y. Morinaka, M. Yoshikawa, T. Amagasa and S. Uemura (2001): The L-index: An Indexing Structure for Efficient Subsequence Matching in Time Sequence Databases. Proceedings of International Workshop on Mining Spatial and Temporal Data, PAKDD-2001: 51-60. 190
8. E. J. Keogh, K. Chakrabarti, M. J. Pazzani, and S. Mehrotra (2001): "Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases" Knowledge and Information Systems 3(3): 263-286. 190
9. S. Z. Selim and M. A. Ismail (1984): K-means-type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(1): 81-87. 190
10. J. C. Bezdek (1981): Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum Press, New York. 190
11. A. P. Dempster, N. M. Laird, and D. B. Rubin (1977): Maximum likelihood from incomplete data via the EM algorithm. J. of Royal Statistical Society Series B, 39: 1-38. 190
12. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan (1998): Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. Proceedings of ACM SIGMOD International Conference on Management of Data: 94-105. 190
13. S. Guha, R. Rastogi, and K. Shim(1998): CURE: An Efficient Clustering Algorithm for Large Databases. Proceedings of ACM SIGMOD International Conference on Management of Data: 73-84. 190
14. T. Zhang, R. Ramakrishnan, and M. Livny (1996): BIRCH: An Efficient Data Clustering Method for Very Large Databases. Proceedings of ACM SIGMOD International Conference on Management of Data: 103-114. 190
15. M. R. Anderberg (1973): Cluster Analysis for Applications. Academic Press, New York. 190
16. R. H. Shumway and D. S. Stoffer (2000): Time Series Analysis and Its Applications. Springer-Verlag, New York.
17. F. Mokhtarian and A. K. Mackworth (1986): Scale-based Description and Recognition of planar Curves and Two Dimensional Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(1): 24-43. 191
18. URL: http://lisp.vse.cz/challenge/ecmlpkdd2002/ 195