Survey on Exiting Method for Selecting Initial Centroids in K-means Clustering

Abstract- Clustering is one of the Data Mining tasks that can be used to cluster or group objects on the basis of their nearness to the central value. K-means clustering algorithm is a one of the major cluster analysis method that is commonly used in practical applications for extracting useful information in terms of grouping data. But the standard K-means algorithm is computationally expensive by getting centroids that provide the quality of the clusters in results. This paper presents the various methods evolved by researchers for finding initial clusters for K Means