Abstract: “Clustering is the process of grouping data into a set of disjoint classes called cluster. It is an effective technique used to classify collection of data into groups of related objects. K-means clustering algorithm is one of the most widely used clustering techniques. The main puzzle of K-means is initialization of centroids. Clustering performance of the K-means totally depends upon the correctness of the initial centroids. In general, K-means randomly selects initial centroids which often show in poor clustering results. This paper has proposed a new approach to optimizing the designation of initial centroids for K-means clustering. We propose a new approach for selecting initial centroids of K-means based on the weighted score of the dataset. According to our experimental results the new approach of K-means clustering algorithm reduces the total number of iterations, improve the time complexity and also it has the higher accuracy than the standard k-means clustering algorithm.”

Keywords: clustering, K-means algorithm,Weighted Score, Data analysis, Initial centroids, Improved K-means