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Feature selection studies reveal how to select a subset or list of attributes or variables that are used to construct models describing data. A feature-selection algorithm is part of the classification rule. This is why feature selection must be included when using cross-validation error estimation. Rough Sets theory provides a new mathematical tool to deal(More)
Visual methods have been extensively studied and performed in cluster data analysis. Given a pairwise dissimilarity matrix D of a set of n objects, visual methods such as Enhanced-Visual Assessment Tendency (E-VAT) algorithm generally represent D as an n × n image I(D) where the objects are reordered to expose the hidden cluster structure as dark blocks(More)
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