# Clustering and manifold learning

I was at a group study recently, and I think one of the points made was that clustering is like 0-dimensional manifold-learning. Is this right? What is the reason behind it?

In manifold learning we have data in $R^n$, and we want to learn a lower dimensional manifold that the data is close to lying on.