Scalable rule-based gene expression data classification

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2008, Pages 1062-1071

  Iwen, Mark A ^a   Lang, Willis ^a   Patel, Jignesh M ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; BIOACTIVITY; BOOLEAN ALGEBRA; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; DATA MINING; DECISION SUPPORT SYSTEMS; GENE EXPRESSION; INFORMATION MANAGEMENT; LEARNING SYSTEMS; MINING; SEARCH ENGINES; TECHNOLOGY;

ASSOCIATION RULE MINING; BOOLEAN ASSOCIATION RULES; BOOLEAN STRUCTURE; CLASSIFICATION ACCURACIES; COMPUTATIONALLY EXPENSIVE; CROSS VALIDATIONS; CURRENT STATE; DATA ENGINEERING; DATA-MINING PROBLEMS; DATA-SETS; GENE EXPRESSION DATA; GENE EXPRESSION DATASETS; HEURISTIC RULES; INTERNATIONAL CONFERENCES; LARGE DATASETS; ORDERS-OF-MAGNITUDE; PARAMETER TUNING; POTENTIAL APPLICATIONS; RULE-BASED; RULE-BASED CLASSIFIERS; RULE-BASED METHODS; RULE-BASED TECHNIQUES; TRAINING DATA; TRAINING DATA SETS; TWO PHASES; WORST CASE;

LAWS AND LEGISLATION;

EID: 52649106393     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2008.4497515     Document Type: Conference Paper

References (25)

1. G. Cong, K. L. Tan, A. K. H. Tung, and X. Xu, "Mining top-k covering rule groups for gene expression dala," SIGMOD, 2005.
2. G. Cong, A. K. H. Tung, X. Xu, R Pan, and J. Yang, "Farmer: Finding interesting rule groups in microarray datasets," SIGMOD, 2004.
3. J. Wang, J. Han, and J. Pei, "Closet+: Searching for the best strategies for mining frequent closed itemsets," KDD, 2003.
4. M. Zaki and C. Hsiao, "Charm: An efficient algorithm for closed association rule mining," Proc. of the 2nd SIAM Int. Conf. on Data Mining (SDM), 2002.
5. R. Agrawal, T. Imielinski, and A. Swami, "Mining associations between sets of items in large databases," SIGMOD, pp. 207-216, 1993.
6. G. Dong, X. Zhang, L. Wong, and J. Li, "Caep: Classification by aggregating emerging patterns," Proc. 2nd Int. Conf. Discovery Science (DS), 1999.
7. J. Li and L. Wong, "Identifying good diagnostic genes or gene groups from gene expression data by using the concept of emerging patterns," Bioinformatics, vol. 18, pp. 725-734, 2002.
8. C. Cortes and V. Vapnik. "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
9. J. R. Quinlan, "Bagging, boosting, and c4.5," AAAI, vol. 1, pp. 725-730, 1996.
10. B. Liu, W. Hsu, and Y. Ma, "Integrating classification and association rule mining," KDD, 1998.
11. T. Mcintosh and S. Chawla, "On discovery of maximal confident rules without support pruning in microarray data," SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD), 2005.
12. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," VLDB, pp. 487-199, 1994.
13. Available at http://www-personal.umich.edu/~markiwen/.
14. R. Motwani and P. Raghavan, Randomized Algorithms. Cambridge University Press, 1995.
15. S. Sudarsky, "Fuzzy satisfiability," Intl. Conf. on Industrial Fuzzy Control and Intelligent Systems (IFIS), 1993.
16. Available at http://sdmc.i2r.a-star.edu.sg/rp/.
17. "The dprep package," http://cran.r-project.org/doc/packages/ dprep.pdf.
18. C. Chang and C. Lin, "Libsvm: a library for support vector machines," 2007. [Online]. Available: www.csie.ntu.edu.tw/cjlin/papers/ libsvm.pdf
19. L. Breiman, "Random forests." Mack Learn., vol. 45, no. 1, pp. 5-32, 2001.
20. W. Li, J. Han, and J. Pei, "Cmar: Accurate and efficient classification based on multiple class-association rules," ICDM, 2001.
21. F. Rioult, J. F. Boulicaut, B. Cremilleux, and J. Besson, "Using transposition for pattern discovery from microarray data," DMKD, pp. 73-79, 2003.
22. J. Li, X. Zhang, G. Dong, K. Ramamohanarao, and Q. Sun, 'Efficient mining of high confidence association rules without support thresholds," Principles of Data Mining and Knowledge Discovery (PKDD), pp. 406 -411, 1999.
23. G. Dong and J. Li, "Efficient mining of emerging patterns: discovering trends and differences," KDD, pp. 43-52, 1999.
24. S. Vinterbo, E. Kim, and L. Ohno-Machado, "Small, fuzzy and interpretable gene expression based classifiers," Bioinformatics, vol. 21, pp. 1964-1970, 2005.
25. S.-Y. Ho, C.-H. Hsieh, H.-M. Chen, and H.-L. Huang, "Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis," Biosystems, vol. 85, pp. 165-176, 2006.