Performance Comparison of FSA Red & Apriori Algorithm’s in Mutation Analysis
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2014 by IJCTT Journal|
|Year of Publication : 2014|
|Authors : Mayilvaganan M, Hemalatha R|
|DOI : 10.14445/22312803/IJCTT-V17P139|
Mayilvaganan M, Hemalatha R "Performance Comparison of FSA Red & Apriori Algorithm’s in Mutation Analysis". International Journal of Computer Trends and Technology (IJCTT) V17(4):205-209, Nov 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
In this paper the attempt has been made to analyze the DNA gene cancer dataset with RBC ,WBC and platelet cancer data set. The basic idea behind this proposed method is comparing the 3 large nucleotide DNA dataset with with Bloom filter and discovering the matched subsequence. To validate the proposed algorithm, association and classification rule based on the FSA red algorithm with bloom filters and apriori algorithm using hierarchical clustering are compared using data mining technique. Here this algorithm is applied to find no of sequence occurrences and mutation analysis for the 3 nucleotide DNA gene dataset. In order to evaluate the proposed methodology, Comparisons are made based on the Execution time and memory efficiency in finding frequent patterns. The extracted rules and analyzed results are graphically demonstrated. The performance is analyzed based on the different no of instances and confidence in DNA sequence data set.
 Role of Association Rule Mining in Numerical Data Analysis Sudhir Jagtap, Kodge B. G., Shinde G. N., Devshette P. M
 M.Anandavalli, M.K.Ghose ,K.Gauthaman,”Association Rule Mining in Geonomics”,International journal of Computer Theory and Engineering Vol.2 ,No.2 April 2010.
 Piatetsky-Shapiro, G. (1991), Discovery, analysis, and presentation of strong rules, in G. Piatetsky-Shapiro & W. J. Frawley, eds, ‘Knowledge Discovery in Databases’, AAAI/MIT Press, Cambridge, MA.
 Role of association rule mining in numerical data analysis, sudhir Sudhir Jagtap, Kodge B. G., Shinde G. N., Devshette P. M
 Bayardo, Roberto J., Jr.; Agrawal, Rakesh; Gunopulos, Dimitrios (2000). "Constraint-based rule mining in large, dense databases". Data Mining and Knowledge Discovery (2): 217–240. doi:10.1023/A:1009895914772.
 Webb, Geoffrey I. (2000); Efficient Search for Association Rules, in Ramakrishnan, Raghu; and Stolfo, Sal; eds.; Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000), Boston, MA, New York.
 http://www.cogsys.wiai.unibamberg.de/teaching/ss05/ml/slides/cogsysI I-6.pdf
 M.Anandavalli , M.K.Ghose , K.Gouthaman ,”Association Rule Mining in Genomics”,International journal of computer Theory and engineering ,Vol.2,No.2 April,2010.
 Arun.K.Pujari”data mining techniques “,Universities Press (india) private limited.2001.
 F.Braz,”A review of the association rules data mining techniques for the analysis of gene expressions”
 Douglas Trewartha, ”Investigating data mining in MATLAB “,Rhodes University 2006.
Association Rule and Classification, Zero rule, fsa red and Apriori algorithm.