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

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