International Journal of Engineering and Technology

Clustering is the process of combining a set of data in such a way that data in the same group are more similar to each other than the groups (clusters). K-Means is an algorithm for widely used in clustering techniques. But in this algorithm some of the issues are determined i.e. K-value selected by user is the main disadvantage. To overcome the drawback visual methods such as the VAT algorithm generally used for cluster analysis, also it is used to obtain the k-value prior to clustering. But the estimated result does not match with the true (but unknown) value in many cases. Then Spectral VAT algorithm was implemented. This spec-VAT algorithm is more efficient than VAT algorithm for complex data sets. The Spec-VAT based algorithms such as A Spec-VAT, P Spec-VAT and E Spec-VAT is also used to find out the cluster value efficiently. But the range of k value is either directly or indirectly given to spectral based VAT algorithms. In this paper we propose direct visual validation method and divergence matrix. In this proposed work the value of k or the range of k is neither directly nor indirectly specified by the users. Instead of k value, we propose a new method of comparing objects and from that result. We choose an object which is closer than other object, From the V2VAT (Visual Validation VAT) algorithm the experimental result shows that the proposed algorithm is much better than the other algorithms.