On discovery of maximal con-dent rules without support pruning in microarray data.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2005, Pages 37-45

  Mcintosh, Tara ^a   Chawla, Sanjay ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Association rules; Maximum confidence; Microarray; Row enumeration

Indexed keywords

BIOLOGICAL SIGNIFICANCE; DOWNWARD CLOSURE; INTERESTING RULES; MAXIMUM CONFIDENCE; MICROARRAY DATA; ROW-ENUMERATION; RULE MINING ALGORITHMS; SUPPORT MEASURE;

ALGORITHMS; ASSOCIATION RULES; DATA MINING; GENES; MICROARRAYS;

BIOINFORMATICS;

EID: 36249007404     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1134030.1134038     Document Type: Conference Paper

References (19)

1. R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In Proc. ACM S10MOD Int. Conf. on Management of Data, pages 207-216, 1993.
2. T. Akutsu, S. Kuhara, O. Maruyama, and S. Miyano. Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model. Theoretical Computer- Science, 298:235-251, 2003.
3. T. Akutsu, S. Miyano, and S. Kuhara. Identification of genetic networks from a small number of gene expression patterns under the boolean network model. In Pacific Symposium on Biocomputing, pages 17-28, 1999.
4. T. Beissbarth and T. Speed. Gostat: Find statistically overrepresented gene ontologies within gene groups. Bioinformatics, 20(9):1464-1465, 2004.
5. C Alfarano et.al. The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res, 33:D418-24, 2005.
6. G. Cong, K.-L. Tan, A. K. H. Tang, and F. Pan. Mining frequent closed patterns in microarray data. In 4th IEEE International Conference on Data Mining (ICDM 2004h PaScs 363-366, 2004.
7. G. Cong, K.-L. Tan, A. K. H. Tung, and X. Xu. Mining top-k covering rule groups for gene expression data. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 670-681, 2005.
8. G. Cong, A. K. H. Tung, X. Xu, F. Pan, and J. Yang. Farmer: Finding interesting rule groups in microarray datasets. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 143-154, 2004.
9. C. Creighton and S. Hanash. Mining gene expression databases for association rules. Bioinformatics, 19(1):79-86, 2003.
10. R. F. Hassett, A. M. Romeo, and D. J. Kosman. Regulation of high affinity iron uptake in the yeast saccharomyces cerevisiae. J Biol Cherri, 273 (13): 7628-7636, 1998.
11. V. Haurie, H. Boucherie, and F. Sagliocco. The snfl protein kinase controls the induction of genes of the iron uptake pathway at the diauxic shift in saccharomyces cerevisiae. J Biol Cherri, 278(46):45391-6, 2003.
12. Y. Huang, H. Xiong, S. Shekhar, and J. Pci. Mining confident colocation rules without a support threshold. In Proc. ACM Symposium on Applied Computing (SAC), pages 497-501, 2003.
13. T. Hughes et al. Functional discovery via a compendium of expression profiles. Cell, 102:109-126, 2000.
14. L. J. Martins, L. T. Jensen, J. R. Simon, G. L. Keller, and D. R. Winge. Metalloregulation of frel and fre2 hornologs in saccharomyces cerevisiae. J Biol Cherry 273(37):23716-23721, 1998.
15. F. Pan, G. Cong, A. K. H. Tung, J. Yang, and M. J. Zaki. Carpenter: finding closed patterns in long biological datasets. In Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 637-642, 2003.
16. J. Pei, J. Han, and R. Mao. Closet: An efficient algorithm for mining frequent closed iternsets. In ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pages 21-30, 2000.
17. F. Rioult, J.-F. Boulicaut, B. Cremileux, and J. Besson. Using transposition for pattern discovery from microarray data. In ACM SIGMOD Workshop Data Mining and Knowledge Discovery, 2003.
18. The Gene Ontology Consortium. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res, 32:D258-D261, 2004.
19. M. J. Zaki and C.-J. Hsiao. Charm: An efficient algorithm for closed itemset mining. In Proc. 2nd SI AM International Conference on Data Mining, 2002.