International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 32 - Issue 4 |
Published: October 2011 |
Authors: T.Chandrasekhar, K.Thangavel, E.Elayaraja |
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T.Chandrasekhar, K.Thangavel, E.Elayaraja . Effective Clustering Algorithms for Gene Expression Data. International Journal of Computer Applications. 32, 4 (October 2011), 25-29. DOI=10.5120/3893-5454
@article{ 10.5120/3893-5454, author = { T.Chandrasekhar,K.Thangavel,E.Elayaraja }, title = { Effective Clustering Algorithms for Gene Expression Data }, journal = { International Journal of Computer Applications }, year = { 2011 }, volume = { 32 }, number = { 4 }, pages = { 25-29 }, doi = { 10.5120/3893-5454 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2011 %A T.Chandrasekhar %A K.Thangavel %A E.Elayaraja %T Effective Clustering Algorithms for Gene Expression Data%T %J International Journal of Computer Applications %V 32 %N 4 %P 25-29 %R 10.5120/3893-5454 %I Foundation of Computer Science (FCS), NY, USA
Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in Bioinformatics research. In this paper, K-Means algorithm hybridised with Cluster Centre Initialization Algorithm (CCIA) is proposed Gene Expression Data. The proposed algorithm overcomes the drawbacks of specifying the number of clusters in the K-Means methods. Experimental analysis shows that the proposed method performs well on gene Expression Data when compare with the traditional K- Means clustering and Silhouette Coefficients cluster measure.