Efficient K-means Clustering with Greedy Algorithm for Minimum Gene Set Identification in Microarray Data

Abstract

Microarray data analysis is a widely used technique to unravel the gene expression and the ultimate aim is to find the differentially expressed genes in a particular condition. The initial step in a micro array data analysis is to identify the minimum subset of genes that may be differentially expressed, since the target number of genes in a micro array data is thousand of genes that are expressed together. Analyzing thousands of genes from a particular experiment is needless, as only few genes are involved in a particular disorder/dysfunction. Hence, the present research work aims at developing an efficient K-means clustering with greedy algorithm for identification of minimum subset of genes that may be differentially expressed in a micro-array experiment. It was found that the developed algorithm is more effective than the existing methods of gene subset selection.