Body: <p>I am trying to perform a simple k-means clustering of a sample population based on about 30 features. Some of the variables are highly correlated as measured by the Pearson correlation coefficient and I want to eliminate some of them.</p>
  <p>The question is if I have two highly correlated variables A and B, which one do I remove? If this were a supervised problem, then I can go about it a few ways, but it is not clear to me how I would do this in an unsupervised problem.</p>
  <p>I guess I need to measure the importance of A and B by means of some metric(s) and make a decision based on that. So any pointers on such metrics or other ideas would be appreciated.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-26T07:30:01.597Z
Comments.Text: You could try a principal components analysis and pick the first x factors, depending on how much variance they cover and how many features you want for your clustering.
Comments.UserDisplayName: Oliver Mason
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-30T08:42:02.227Z
Comments.Text: @OliverMason I would like to avoid transformations and be able to operate in the original feature space itself. Issue with PCA is the components cannot be used to explain the resulting clusters in terms of the original features from a business perspective.
Comments.UserDisplayName: SAS2Python
Tags: <clustering><feature-selection><correlation>
Title: Clustering: which correlated feature to eliminate
UserDisplayName: SAS2Python
ThreadText: # Clustering: which correlated feature to eliminate
  
  (Asked by: username_0 on 2024-04-26)
  
  I am trying to perform a simple k-means clustering of a sample population based on about 30 features. Some of the variables are highly correlated as measured by the Pearson correlation coefficient and I want to eliminate some of them.
  
  The question is if I have two highly correlated variables A and B, which one do I remove? If this were a supervised problem, then I can go about it a few ways, but it is not clear to me how I would do this in an unsupervised problem.
  
  I guess I need to measure the importance of A and B by means of some metric(s) and make a decision based on that. So any pointers on such metrics or other ideas would be appreciated.
  
  **Tags:** clustering, feature-selection, correlation
  
  > **Comments:**
  > **username_1**: You could try a principal components analysis and pick the first x factors, depending on how much variance they cover and how many features you want for your clustering.  
  > **username_0**: @username_1 I would like to avoid transformations and be able to operate in the original feature space itself. Issue with PCA is the components cannot be used to explain the resulting clusters in terms of the original features from a business perspective.  
  
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  ## 0 answers
  
  