Journals Proceedings

Abstract

This paper describes the clustering analysis of microarray gene expression data. Microarray basically consists of large number of gene sequences under multiple conditions. This microarray technology has made it possible to concurrently monitor the expression levels of thousands of genes and across collection of related samples. The most important area of microarray technology is the data clustering analysis. Cluster analysis refers to partitioning a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. Many conventional clustering algorithms like K-means, FCM, hierarchical techniques are used for gene expression data clustering. But PSO based K-means gives better accuracy than these existing algorithms. In this paper, a Particle Swarm Optimization (PSO)-based K-means clustering algorithm has been proposed for clustering microarray gene expression data.