Unskewing the Data with the PCA's Help I'm making some RFM Analyses (Customer Segmentation) and, in order to feed the RFM data to K-Means, I need to unskew the data, as K-Means works best when dealing with symmetrical distributions.

One of the best practices I've found so far is to use log transforms in order to unskew the data. However, after a bit of experimenting (and, hopefully, correct intuition), I've also found that PCA is working really well at unskewing the data, without the need of performing a log transform. Is this supposed to work or am I in danger of making incorrect assumptions?

On a (cough) safer bet, I was planning on using PCA after a log transform and the scaling of the data, since it would help to orthogonalize it and, I suppose, improve K-Means's performance. Is this better or worse than simply applying PCA without the log transform?
On a (cough) safer bet, I was planning on using PCA after a log transform and the scaling of the data, since it would help to orthogonalize it and, I suppose, improve K-Means's performance. Is this better or worse than simply applying PCA without the log transform?


This is your solution, this is the normal, most used and best practice on what to do with skewed data