Effective Clustering Algorithms for Gene Expression Data

Details Effective Clustering Algorithms for Gene Expression Data Effective Clustering Algorithms for Gene Expression Data

2012-01-24

arxiv.org/abs/1201.4914v1

International Journal of Computer Applications (0975 - 8887) Volume 32 - No.4, October 2011

Computer Science

Computational Engineering, Finance, and Science

5 pages

Scientific paper

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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