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While principal component analysis methods have been classically used to split high-dimensional datasets into clusters by aggregating individual data onto typical observations (the cluster centers), archetypal analysis depicts individual behavior more as a continuum within an archetypal landscape defined by extreme strategies: the archetypes

Cigarette smoking and white matter microstructure

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