New Approach for K-mean and K-medoids Algorithm

Abstract

K-means and K-medoids clustering algorithms are widely used for many practical applications. Original k-mean and kmedoids algorithms select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it generates unstable and empty clusters which are meaningless. The original k-means and k-mediods algorithm is computationally expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations. The new approach for the k mean algorithm eliminates the deficiency of exiting k mean. It first calculates the initial centroids k as per requirements of users and then gives better, effective and stable cluster. It also takes less execution time because it eliminates unnecessary distance computation by using previous iteration. The new approach for kmedoids selects initial k medoids systematically based on initial centroids. It generates stable clusters to improve accuracy.