Multidimensional scaling is designed to preserve the most variance (or some variation of that idea) among points in a lower dimensional space. So for the most p

Multidimensional scaling is designed to preserve the most variance (or some variation of that idea) among points in a lower dimensional space. So for the most part, the cluster memberships assigned by clustering in an n-dimensional space are the same in the lower dimensional projection. There's often no need to cluster in a higher space because the outcome is the same, and clustering is more expensive when there's more dimensions.