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Summary Data Mining Unsupervised Learning R58,86
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Summary Data Mining Unsupervised Learning

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This document contains a summary of the theory seen in the lab sessions. In addition, it also contains solutions to the exercises of the lab sessions.

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  • August 13, 2023
  • 10
  • 2022/2023
  • Summary
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Unsupervised learning
PRINCIPAL COMPONENTS ANALYSIS

apply(): allows us to apply a function (here mean() function)




1 = mean of the rows, 2 = mean of the columns

prcomp(): performs principal components analysis




By default, prcomp() will center the variables to have mean zero

pr.out$rotation: provides principal component loadings




prcomp(): no need to multiply data by principal component loading vectors in order

to obtain the principal component score vectors => x has its columns principal

component score vectors(kth column is kth principal component score vector)




plot first two principal components




Scale = 0 : ensures that arrows are scaled to represent the loadings

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