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The\n                    <jats:italic>K<\/jats:italic>\n                    \u2010means method is first introduced, various formulations of the minimum variance loss function and alternative loss functions within the same class are outlined, and different methods of choosing the number of clusters and initialization, variable preprocessing, and data reduction schemes are discussed. Theoretic statistical results are provided and various extensions of\n                    <jats:italic>K<\/jats:italic>\n                    \u2010means using different metrics or modifications of the original algorithm are given, leading to a unifying treatment of\n                    <jats:italic>K<\/jats:italic>\n                    \u2010means and some of its extensions. 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