Since Principal components capture most of the information, clustering on them should provide similar result as that of the clustering on the original data.
As such, it seems to me (who's not a statistician, but interested nonetheless) like principal components would be better suited to showcase natively existing clusters since the collinearity would be eliminated.
But are there situations where clustering on PCs may not be as good and may provide worse results than the original data set?
I can think of a situation where, having many correlated columns and the cluster having to be biased towards this component, can yield a worse result. Is this a common occurrence? If so, this seems to me like distributing weights without actually understanding it.
Can an expert throw some light to intuitively understand?