Clustering is the problem of partitioning a set of unlabeled objects O = {o1,…, on} into self-similar groups. In conventional (object data) cluster analysis, the objects are separated into groups according to their features. Although clustering is typically thought of as only the act of separating objects into the proper groups, cluster analysis actually consists of three concise questions: Cluster tendency, Partitioning and Cluster validity. In this paper, we present a efficient formulation of the iVAT algorithm which significantly reduces its computational complexity. Our iVAT implementation begins by finding the VAT reordered dissimilarity matrix and then performs a distance transform on this matrix. The VAT algorithm is a visual method for determining the possible number of clusters in, or the cluster tendency of a set of objects. To improve the clustering accuracy we present scoVAT algorithm and to improve the effectiveness of the VAT. We present an efficient formulation of the scoVAT algorithm which reduces the computational complexity.