A Novel Approach for Determination of Clusters from Unlabeled Data Sets

etermination of clusters from unlabeled data sets. we investigate a new method called Extended Support vector Machine(ESVM)along with existing Dark Block Extraction (DBE) which is based on an existing algorithm for Visual Assessment of Cluster Tendency (VAT) of a data set, using several common image and signal processing techniques. Its basic steps include 1)Generating a VAT image of an input dissimilarity matrix, 2)Performing image segmentation on the VAT image to obtaina binary image, followed by directional morphologicalfiltering, 3)Applying a distance transform to the filtered binary image and projecting the pixel values onto the maindiagonal axis of the image to form a projection signal, 4)Smoothing the projection signal, computing its First-order derivative and then detecting major peaks and valleys in the resulting signal to decide the number of clusters, and 5)TheK-Means algorithm is applied to the major peaks. We alsoimplement the Cluster Count Extraction (CCE),which uses VAT and the combination of several imageprocessing techniques. In both the methods we use Reordered Dissimilarity Image (RDI), which highlights potential clustersas a set of “Dark blocks” along the diagonal of the image,corresponding to sets of objects with low issimilarity, whichis implemented using VAT algorithm.