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Mining Spatial Gene Expression Data Using Association Rules
M.Anandhavalli, M.K.Ghose, K.Gauthaman
Pages - 351 - 357     |    Revised - 26-11-2009     |    Published - 26-12-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
Association Rule, Spatial Gene expression data, Similarity Matrix, Boolean vector
One of the important problems in data mining is discovering association rules from spatial gene expression data where each transaction consists of a set of genes and probe patterns. The most time consuming operation in this association rule discovery process is the computation of the frequency of the occurrences of interesting subset of genes (called candidates) in the database of spatial gene expression data. A fast algorithm has been proposed for generating frequent itemsets without generating candidate itemsets along with strong association rules. The proposed algorithm uses Boolean vector with relational AND operation to discover frequent itemsets. Experimental results shows that combining Boolean Vector and relational AND operation results in quickly discovering of frequent itemsets and association rules as compared to general Apriori algorithm .
CITED BY (5)  
1 OO, O. Knowledge Discovery from Students’ Result Repository: Association Rule Mining Approach. International Journal of Computer Science and Security (IJCSS), 149(2), 199.
2 Oyelade, O. J., & Oladipupo, O. O. Knowledge Discovery from Students’ Result Repository: Association Rule Mining Approach.
3 Elayidom, S., Idikkula, S. M., & Alexander, J. Applying Statistical Dependency Analysis Techniques In a Data Mining Domain.
4 Anandhavalli, M., Ghose, M. K., Gauthaman, K., & Boosha, M. (2010). Global search analysis of spatial gene expression data using genetic algorithm. In Recent Trends in Network Security and Applications (pp. 593-602). Springer Berlin Heidelberg.
5 Chandra, E., & Nandhini, K. (2010). Knowledge mining from student data. European Journal of Scientific Research, 47(1), 156-163.
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A Savasere, E. Ommcinskl and S Navathe, “An efficient algorithm for mining association rules in large databases”, In Proceedings Of the 21st International Conference on Very Large Databases, Zurich, Switzerland, September 1995.
Agrawal, R. & Srikant, R., “Fast Algorithms for Mining Association Rules in large databases”. In Proceedings of the 20th International Conference on Very Large Databases pp. 487-499. Santiago, Chile, 1994.
Agrawal, R., Imielinski, T., & Swami, A., ”Mining association rules between sets of items in large databases”. Proceedings of the ACM SICMOD conference on management of data”, Washington, D.C, 1993.
Baldock,R.A., Bard,J.B., Burger,A., Burton,N., Christiansen,J., Feng,G., Hill,B., Houghton,D., Kaufman,M., Rao,J. et al., “EMAP and EMAGE: a framework for understanding spatially organized data”, Neuroinformatics, 1, 309–325, 2003.
J S. Park and M -S. Chen and PS. Yu, “An effective hash-based algorithm for mining association rules”, Proceedings of the ACM SIGMOD International Conference on Management of Data", San Jose, CA, May 1995.
Pang-Ning Tan, Micahel Steinbach, Vipin Kumare, ”Intoduction to Data Mining Pearson Education”, second edition, pp.74, 2008.
Xu, Z. & Zhang, S., “An Optimization Algorithm Base on Apriori for Association Rules”. Computer Engineering 29(19), 83-84, 2003.
Associate Professor M.Anandhavalli
SMIT - India
Dr. M.K.Ghose
- India
Mr. K.Gauthaman
- India