DocumentCode :
3180336
Title :
A novel parallel hybrid K-means-DE-ACO clustering approach for genomic clustering using MapReduce
Author :
Bhavani, R. ; Sadasivam, G. Sudha ; Kumaran, Radhika
Author_Institution :
Dept. of CSE, PSG Coll. of Technol., Coimbatore, India
fYear :
2011
fDate :
11-14 Dec. 2011
Firstpage :
132
Lastpage :
137
Abstract :
The main aim of this paper is to design a scheme to identify the species from its genome sequence. Feature descriptors for a genome sequence are identified using MapReduce framework. Each feature descriptor is a three lettered keyword generated using A, T, C, G nucleotide bases. Genome sequences of related species are clustered by considering the feature descriptor count. MapReduce version of clustering model that uses K-means, Differential Evolution (DE) and Ant Colony Optimization (ACO) has been proposed. This MapReduce model improves accuracy as the entire genome sequence is considered. The inherent parallelism in the MapReduce model also enhances execution time efficiency.
Keywords :
ant colony optimisation; biology computing; distributed processing; evolutionary computation; genomics; pattern clustering; A-T-C-G nucleotide bases; MapReduce framework; ant colony optimization; differential evolution; feature descriptors; genome sequence; genomic clustering; parallel hybrid k-means-DE-ACO clustering approach; species identification; Bioinformatics; Biological cells; Clustering algorithms; DNA; Feature extraction; Genomics; Vectors; Ant Colony Optimization; Differential Evolution; Genomic clustering; K-means clustering; MapReduce;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Communication Technologies (WICT), 2011 World Congress on
Conference_Location :
Mumbai
Print_ISBN :
978-1-4673-0127-5
Type :
conf
DOI :
10.1109/WICT.2011.6141231
Filename :
6141231
Link To Document :
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