A new approach to improve the clustering accuracy using informative genes for unsupervised microarray data sets

DNA microarray technology can be used to measure expression levels for thousands of genes in a single experiment across different samples. Within a gene expression matrix there are usually several particular Macroscopic Phenotypes of samples related to some diseases or drug effects such as diseased samples, normal samples or drug treated samples. The goal of sample based clustering is to find the phenotype structure or substructure of the samples. Currently most of research work focuses on the supervised analysis, relatively less attention has been paid to unsupervised approaches in sample based analysis which is important when domain knowledge is incomplete or hard to obtain. The standard k-means algorithm is effective in producing clusters for many practical applications. But the computational complexity of the original k-means algorithm is very high in high dimensional data and the accuracy of the clustering result depends on the initial centroid. In this paper, we present a new framework for unsupervised sample based clustering using informative genes for microarray data. We proposed a method to find initial centroid for k-means and we have used similarity measure to find the informative genes. The goal of our clustering approach is to perform better cluster discovery on sample with informative gene.