title: scikit-learn Standard Scaler - get the standard deviation in the original unscaled space for GMM
body: <p>Before running a GMM clustering model, I use a standard Scaler to transform my data into a 0 mean,  1 std dataset</p>
  
  <p>Having then performed clustering, I am interested in representing the learned cluster back in the original space rather than the 0-mean, 1 standard deviation, where the feature values make more sense.</p>
  
  <p>Is it then correct to do the following:</p>
  
  <ol>
  <li><p>Get the mean by multiplying the mean of each GMM cluster by the
  scaler.mean_ parameters.</p></li>
  <li><p>Get the standard deviation by multiplying the square of the
  diagonal covariance matrix by the scaler.std_ parameters.</p></li>
  </ol>
  
  <p>I'd appreciate any feedback,</p>
  
  <p>Thank you!</p>