An effective method determining the initial cluster centers for K-means for clustering gene expression data

Abstract

Clustering is an important tool for analyzing gene expression data. Many clustering algorithms have been proposed for the analysis of gene expression data. In this article we have clustered real life gene expression data via K-Means which is one of clustering algorithms. Also, we have proposed a new method determining the initial cluster centers for K-means. We have compared results of our method with other clustering algorithms. The comparison results show that the K-means algorithm which uses the proposed methods converges to better clustering results than other clustering algorithms.