Selection of Initial Centroids for k-Means Algorithm

Clustering is one of the important data mining techniques. k-Means (1) is one of the most important algorithm for Clustering. Traditional k-Means algorithm selects initial centroids randomly and in k-Means a lgorithm result of clustering highly depends on selection of initial centroids. k-Means algorithm is sensitive to initia l centroids so proper selection of initial centroids is necessa ry. This paper introduces an efficient method to start the k -Means with good initial centroids. Good initial centroids are useful for better clustering.