Algorithms such as T-SNE and PCA exists. They're used to identify/create clusters in higher dimensions and project them down to lower dimensional space.
However, lets go somewhat the other way around; I have a 2D plane with points making up clusters. However, they're too tightly packed and unevenly spread out over the plane.
Is there a method to spread these out more evenly over the plane? (so that the distances between a point and its closest ones are inside smaller interval than for a normal cluster) I still want to keep information regarding closeness by having clusters look like they're sampled from connected regions of the plane.