Research Article

Effective Clustering Algorithms for Gene Expression Data

by  T.Chandrasekhar, K.Thangavel, E.Elayaraja
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Issue 4
Published: October 2011
Authors: T.Chandrasekhar, K.Thangavel, E.Elayaraja
10.5120/3893-5454
PDF

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
Abstract

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.

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Clustering CCIA K-Means Gene expression data

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