the title might not be the best to adress my question. Here is my problem

I have a data set with 21 features. and I want to cluster the data to interpret if there are any insights that I can have by clustering the data.

I started the process with PCA and reduced it to 2 component and then trained k-means clustering model with 4 central points.

When I visualize it, my clusters look nice and tidy based on 2 components. The problem that I can not solve is how I can go from those two components back to the 21 features in my original set.

That is important since I need to seee which of those features are important and make an analysis through those features instead of those 2 components.



1 Answer 1


If you have JMP, you will have a clear picture of varibales corresponding to the components. JMP shows loadings of varibales for each component. You can see varibales loadings foe each component in R using pca$rotation

  • $\begingroup$ interesting. hope there is a scikitlearn equivalent of this $\endgroup$ Apr 17, 2023 at 6:14
  • $\begingroup$ Check this post on stackoverflow for a lot of detailed discussion - I think it answers your question: stackoverflow.com/questions/47370795/… $\endgroup$ Apr 17, 2023 at 7:07

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