title: Does correlation important factor in Unsupervised learning (Clustering)?
body: <p>I am working with the dataset of size (500, 33). </p>
  
  <p>In particular the data set contains 9 features say</p>
  
  <p><code>[X_High, X_medium, X_low, Y_High, Y_medium, Y_low, Z_High, Z_medium, Z_low]</code></p>
  
  <p>Both visually &amp; after correlation matrix calculation I observed that </p>
  
  <p>[X_High, Y_High, Z_High] &amp; [ X_medium, Y_medium,  Z_medium ] &amp; [X_low, Y_low, Z_low] are highly correlated (above 85%).</p>
  
  <p>I would like to perform a Clustering algorithm (say K means or GMM or DBSCAN).</p>
  
  <p>In that case,</p>
  
  <p>Is it necessary to remove the correlated features for Unsupervised learning ?
  Whether removing correlation or modifying features creates any impact ?</p>