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I want to get the Principal components of a dataset and apply K mean clustering on them. Do I need to Normalized the PCA output before applying Kmeans on them ?

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  • $\begingroup$ If the ranges of the numeric attributes differ by many orders of magnitude, then yes. You could also scale (divide each by standard deviation in that column for instance) $\endgroup$
    – knb
    Apr 11 '18 at 10:56
  • $\begingroup$ Also you must scale the data before applying PCA. I hope you know that! $\endgroup$
    – spectre
    2 days ago
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No - There is no need to normalize after Principal Component Analysis (PCA) because each dimension is on the same scale.

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