ENHANCED VAT FOR CLUSTER QUALITY ASSESSMENT IN UNLABELED DATASETS

Abstract

The increased demand for clustering objects of unlabeled data into similarity group lies in determining a number of clusters. In addition, the performance of the cluster should be analyzed to provide the precise clustering of objects. Available clustering algorithm depends on the number of clusters C to search with threshold. The proposed method of this paper is Enhanced Visual Assessment of Cluster Tendency, which robotically identifies the number of object groups or clusters in unlabeled datasets. The proposed algorithm relies on visual assessment of cluster tendency (VAT) that intermingles Euclidean, Mahalanobis distance measures, and common image processing techniques. Enhanced VAT produces a binary image, which can be visually assessed for the cluster tendency. However, VAT becomes disrupting for huge datasets. Enhanced VAT reduces the amount of computation and performs dissimilarities with different measures of metrics that are used for an effective visual evaluation process. Validation of our algorithm is performed on several UCI datasets and HIV real world datasets.