Initial centroid selection for K- means clustering algorithm using the statistical method

Abstract

An iterative process that converges to one of the many local minima is used in practical clustering methods. K-means clustering is one of the most well-liked clustering methods. It is well known that these iterative methods are very susceptible to the initial beginning circumstances. In order to improve K-means clustering's performance, this research suggests a novel method for choosing initial centroids. The suggested approach is evaluated with online access records, and the results demonstrate that better initial starting points and post-processing cluster refinement result in better solutions.