Answers.Body: <p>Essentially, yes. One feature predicts to a reasonably high degree what the other features look like, so the additional features have limited discriminatory power. Obviously there is some effect, as they don't correlate perfectly, but it's minimal.</p>
  <p>I would look for some other features if possible.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-05-05T11:18:17.417Z
Answers.LastActivityDate: 2022-05-05T11:18:17.417Z
Answers.UserDisplayName: Oliver Mason
Body: <p>I know that when two features are highly correlated with each other, one of them should be removed from the dataset so they don't add twice the weight. However, what if all my features share a correlation?</p>
  <p>For example, when I calculate the Pearson coefficients I get:</p>
  <p><a href="https://i.sstatic.net/xKGg8.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/xKGg8.png" alt="enter image description here" /></a></p>
  <p>Does this mean that only one feature is actually relevant when I start clustering the dataset?</p>
  
Tags: <machine-learning><unsupervised-learning><clustering>
Title: What happens if all the features are correlated with each other before clustering?
UserDisplayName: Sanzor
ThreadText: # What happens if all the features are correlated with each other before clustering?
  
  (Asked by: username_0 on 2022-05-05)
  
  I know that when two features are highly correlated with each other, one of them should be removed from the dataset so they don't add twice the weight. However, what if all my features share a correlation?
  
  For example, when I calculate the Pearson coefficients I get:
  
  [![enter image description here](https://i.sstatic.net/xKGg8.png)](https://i.sstatic.net/xKGg8.png)
  
  Does this mean that only one feature is actually relevant when I start clustering the dataset?
  
  **Tags:** machine-learning, unsupervised-learning, clustering
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-05-05. Score: 2 | Accepted answer
  
  Essentially, yes. One feature predicts to a reasonably high degree what the other features look like, so the additional features have limited discriminatory power. Obviously there is some effect, as they don't correlate perfectly, but it's minimal.
  
  I would look for some other features if possible.
  
  
  ---
  
  