Reducing Runtime Values in Minimum Spanning Tree Based Clustering by Visual Access Tendency

Clustering has been widely used in data analysis. Dissimilarity assesses the distance between objects and this is important in Minimum Spanning Tree (MST) based clustering. An inconsistent edge is identified and removed without knowledge of prior tendency in MST based clustering, which explore the results of clusters in the form of sub-trees. Clustering validity is to be checked at every iterated MST clusters by Dunn’s Index. Higher Dunn’s Index imposes the exact clustering. The existing system takes more run timewhen there are several iterations where as the proposed system takes single step with very less run time. Key contribution of the paper is to find prior tendency in MST Based Clustering by Visual Access Tendency (VAT) and to find clustering results in a single step instead of several trails. The proposed method extends the MST based clustering algorithm with VAT procedure, called as VAT-Based-MST-Clustering. Results are tested on synthetic data sets, and real data sets to conclude the clustering results are improved by proposed method with respect to the runtime.