Microarray Gene Expression Data Clustering Using Red Black Tree Based K-Means Algorithm

Abstract

The need of high quality clustering is very important in the modern era of information processing. Clustering is one of the most important data analysis methods and the k-means clustering is commonly used for diverse applications. Despite its simplicity and ease of implementation, the k-means algorithm is computationally expensive and the quality of clusters is determined by the random choice of initial centroids. Different methods were proposed for improving the accuracy and efficiency of the k-means algorithm. In this paper, we propose a new approach that improves the accuracy of clustering microarray based gene expression data sets. In the proposed method, the initial centroids are determined by using the Red Black Tree and an improved heuristic approach is used to assign the data items to the nearest centroids. Experimental results show that the proposed algorithm performs better than other existing algorithms.