An efficient unified K-Means clustering technique for Microarray Gene expression data.(Report)

INTRODUCTION

The advent of microarray technology made it possible to monitor the expression levels of thousands of genes concurrently whereas in traditional approaches one can focus local examination and collection of data on single gene (Wilkin and Huang, 2007; Chen et al., 2005). Microarray may be used to measure gene expression in many ways, but one of the most popular applications is to compare expression of a set of genes from a cell maintained in a particular 'condition A' to the same set of genes from a reference cell maintained under normal 'condition B'. The process data, after the normalization procedure, can be represented in the form of matrix. Each row in the matrix corresponds to a particular gene and each column could either correspond to an experimental condition or to a specific time point at which expression of genes has been measured. Huge volume of data generated by microarray techniques are collected and stored in massive databases. Traditional techniques and tools are not adequate to deal with this data and obtain the desired results (jiang et al., 2004; Eisen et al., 1998; Ali et al., 2009). The challenge is to effectively analyze and interpret such a huge volume of information. Two statistical operations commonly applied to microarray data are classification and clustering (Suresh et al., 2009; Kumar, 2009). Classification technique is a supervised one in which objects is classified by known class label, whereas clustering is an unsupervised technique requiring no predefined class labels. As we have little knowledge of the complete data set, we have favored unsupervised methods (Eisen et al., 1998). The patterns within the groups are similar to one another and dissimilar to the patterns in different groups. …