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Class mean as simple euclidean distance

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Reference no: EM131549341

K-means (in its common simple form) implicitly assumes that all dimensions are comparable, since it measures distance of a sample to each class mean as simple Euclidean distance. Propose a classification problem in which this is a reasonable assumption (that is, there are input dimensions that are comparable to each other) and one for which it is not (but the dimensions are still numeric, so it would possible to run K-means anyway).

Reference no: EM131549341

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