Review of Existing Methods for Finding Initial Clusters in K-means Algorithm

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. It has found many applications in the field of business, image processing, medical etc. K Means is one the method of clustering which is used widely because it is simple and efficient. The output of the K Means depends upon the chosen central values for clustering. So accuracy of the K Means algorithm depends much on the chosen central values. This paper presents the various methods evolved by researchers for finding initial clusters for K Means. General Terms Accuracy, Centroids, Complexity, Dataset, Initial Clusters, KMeans