On the complexity of finding emerging patterns

Proceedings - International Computer Software and Applications Conference

Volumn 2, Issue , 2004, Pages 126-129

  Wang, Lusheng ^a   Zhao, Hao ^a   Dong, Guozhu ^b   Li, Jianping ^a,c  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b WRIGHT STATE UNIVERSITY   (United States)

^c YUNNAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EMERGING PATTERNS; GENE EXPRESSION; MICROARRAYS; POLYNOMIAL TIME APPROXIMATION;

COMPUTATIONAL COMPLEXITY; DISEASES; GENES; POLYNOMIAL APPROXIMATION; SET THEORY; THEOREM PROVING; VECTORS;

PATTERN RECOGNITION;

EID: 18844458067     PISSN: 07303157     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CMPSAC.2004.1342691     Document Type: Conference Paper

References (11)

1. U. Alon, N. Barkai, D.A. Notterman, K. Gish, S. Ybarra, D. Mack, and A.J. Levine, Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonu-cleotide arrays, Proc. Natl. Acad. Sci. USA 96 (1999) 6745-6750.
2. S. Arora, C. Lund, R. Motwani, M. Sudan and M. Szegedy, Proof verification and hardness of approximation problems, Proceedings of the 33rd IEEE Symp. Found. Comput. Sci. (1992) 14-23.
3. G. Dong and J. Li, Efficient mining of emerging patterns: Discovering trends and differences, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (1999) 43-52.
4. G. Dong and J. Li, Mining Border Descriptions of Emerging Patterns from Dataset Pairs. Submitted to a journal.
5. G. Dong, J. Li, and L. Wong. The Use of Emerging Patterns in the Analysis of Gene Expression Profiles for the Diagnosis and Understanding of Diseases. Chapter in New Generation of Data Mining Applications, eds M. M. Kantardzic and J. Zurada, IEEE Press. To appear.
6. T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesirov, H. Coller, M.L. Loh, J. Downing, M.A. Caligiuri, C.D. Bloomfield, and E.S. Lander, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286 (1999) 531-537.
7. T. Jiang and M. Li, On the approximation of shortest common supersequences and longest common subsequences, SIAM J. Comput. 24 (1995) 137-148.
8. J. Li, K. Ramamohanarao, and G. Dong, The space of jumping emerging patterns and its incremental maintenance algorithms, Proceedings of the 7th International Conference on Machine Learning, (2000) 551-558.
9. J. Li and L. Wong, Identifying good diagnostic gene proups from gene expression profiles using the concept of emerging patterns, Bioinformatics, 18 (2002) 725-734.
10. C.H. Papadimitriou and M. Yannakakis, Optimization, approximation, and complexity classes, J. Comput. System Sci. 43 (1991) 425-440.
11. X. Zhang, G. Dong, and K. Ramamohanarao. Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets. In Proc. ACM KDD, pages 310-314, 2000.