Mrs. A. P. Bhuvaneswari,Dr. C. Shoba Bindu,Dr. R. Praveen Sam,





Cluster analysis refers to the process of combining the group of objects based on similarity features; Traditional methods such as k-means, graphbased clustering etc. are used for clustering of given data objects. Other clustering models, namely, visual access tendency (VAT), cosine based VAT (cVAT), Spectral VAT (SpecVAT), cosine based spectral VAT (cSpecVAT) are more effective because they shows the clustering results with visual evidence for big datasets. These methods compute an initial difference matrix for a set of objects and re-order the same based on ordering of dissimilarity values between objects. Image of re-ordered dissimilarity matrix shows the dark color shaded square blocks along the diagonal, in which each square shaped block represented as a cluster. Synthetic and other benchmarked datasets are taken in the experimental study for proving the efficiency of visual model based clustering approaches.


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