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Clustering analysis is the problem of partitioning a set of objects O = {o1... on} into c self-similar subsets based on available data. In general, clustering of unlabeled data possess three major problems: 1) assessing cluster tendency, i.e., how many clusters to seek? 2) Partitioning the data into c meaningful groups, and 3) validating the c clusters that are discovered. We address the first problem,...
Visual methods have been widely studied and used in data cluster analysis. Given a pairwise dissimilarity matrix D of a set of n objects, visual methods such as the VAT algorithm generally represent D as an n × n image I(D̃) where the objects are reordered to reveal hidden cluster structure as dark blocks along the diagonal of the image. A major limitation of such methods is their inability to highlight...
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