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.
@article{arxiv.1201.4914,
title = {Effective Clustering Algorithms for Gene Expression Data},
author = {T. Chandrasekhar and K. Thangavel and E. Elayaraja},
journal= {arXiv preprint arXiv:1201.4914},
year = {2012}
}