# Kmeans clustering results on pca dataset reduction

I have a set of 321 observations of 18 correlated variables, so I do PCA to extract a low dimensional set of features from this high dimensional data set. I select 9 of 18 components (the number of components that explains 80% of total variance) After determining the number of clusters with NbClust, apply k-means clustering to do the classification.

I am using the PCA for dimensionality reduction in order to reduce the complexity of my problem, given an interpretation to all the components.

My Question: Why are the clusters differentiated only in PC1-other Component plane (example PC1-PC2 plane, PC1-PC3 plane, etc...)? How can I solve this problem?

• You didn't say why it is "problem" necessary to solve for you? You expected different? what you extected then and why? The issue in your Q may be not just about PCA but about how K-means behaves when dimensions are different variance. Dec 13 '16 at 15:58