Evaluation of the Selection of the Initial Seeds for K-Means Algorithm

Clustering method is divided into hierarchical clustering, partitioning clustering, and more. K-Means algorithm is one of partitioning clustering methods and is adequate to cluster a lot of data rapidly and easily. The problem is it is too dependent on initial centers of clusters and needs the time of allocation and recalculation. We compare random method, max average distance method and triangle height method for selecting initial seeds in KMeans algorithm. It reduces total clustering time by minimizing the number of allocation and recalculation.