Paper Title
An Efficient Approach – (KCVD) K-Means Clustering Algorithm With Voronoi Diagram

K-means method is one of the renowned and generally used partitioning clustering technique. However, the major problem with this method is that it cannot ensure the global optimum results due to the random selection of initial cluster center. In this paper, we proposed a clustering algorithm- KCVD (K-MEANS CLUSTERING ALGORITHM WITH VORONOI DIAGRAM) using the concept of Voronoi cells and k-Means. KCVD algorithm brings the hidden data objects in a given data set in picture. As a result, the proposed algorithm automates the selection of initial cluster centroid according to increasing of x-axis, and evaluate the actual cluster value. This helps to analyze the results of high quality data set and are able to identify the noise (disturbance) centroid. These noise centroids will convert into actual cluster value with the help of calculated nearest threshold value. This algorithm reduces the time and space complexity by using stack and pointer. General Terms K-means Clustering, Voronoi cell