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Effective Clustering Algorithms for Gene Expression Data

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International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 1
Year of Publication: 2011
Authors:
T.Chandrasekhar
K.Thangavel
E.Elayaraja
10.5120/3893-5454

T.Chandrasekhar, K.Thangavel and E.Elayaraja. Article: Effective Clustering Algorithms for Gene Expression Data. International Journal of Computer Applications 32(4):25-29, October 2011. Full text available. BibTeX

@article{key:article,
	author = {T.Chandrasekhar and K.Thangavel and E.Elayaraja},
	title = {Article: Effective Clustering Algorithms for Gene Expression Data},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {32},
	number = {4},
	pages = {25-29},
	month = {October},
	note = {Full text available}
}

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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