I find that ~70% of the variance is distributed between the first 5 principal components.
I am guessing that this is not the right analysis to cluster variables into new features. However, there is still a clear clustering if I plot PC1 vs PC2, PC2 vs PC3, PC3 vs PC4 and PC4 vs PC5.
My experience doing PCA is quite recent and I would like to get help on how to interpret these results.
Should I drop further analysis (i.e. feature selection) and switch to another approach, and if so, what would you suggest?