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The aim of clustering is to discover the clusters based on the similarity features of objects. The present algorithm of visual access tendency (VAT) can access an exact number of clusters by its VAT image. The VAT image displays the squared shaped dark blocks along the diagonal; number of cluster information is accessed by counting the number of obtaining square blocks. Other extended versions are...
Self Organizing Map (SOM) is a significant algorithmic methodology to visualize data spaces of larger dimensions. Accurate analysis of the input data requires a well-trained SOM. Many measures are there in practice to analyse the quality of the map. One of the most commonly used measure is Quantization Error. A trained SOM grid with minimum quantization error may not be topologically well preserved...
Gene expression data generated from microarray experiments are characterized by large number of genes or dimensions. Informative gene selection for performing clustering to discover useful phenotypes is a major issue as there is no class information available. In this paper, we propose a wrapper based feature selection approach to perform sample based clustering on gene expression data. The proposed...
A new fuzzy c-means clustering with non-extensive entropy regularization is proposed in this paper. The purpose of entropy regularization is to form approximate solutions of singular problems in the maximum entropy framework. The non-extensive entropy with Gaussian gain is generally used for identifying non-uniform probability densities as in regular texture patterns. It is thus well suited for regularizing...
A video summarization technique is proposed in this work using minimal spanning tree (MST) of data points. The data points correspond to image frames of a shot in the video which is to be summarized. Correlation is chosen as a similarity metric for computing the edge weights of the MST. The representative frames for each shot are chosen by computing the density of each data point. A novel method for...
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