Next, we apply a technique called Multi-Dimensional Scaling (MDS) through scikit-learn (v0.19.0) in order to reduce our N xN dimensional space into a 2D space, where we can then apply clustering techniques. MDS is a method which transforms our similarity matrix and projects each object (i.e pose) into a lower dimensional space in such a way that the distance between objects (i.e poses) are preserved as best as possible. In other words, when we project into a 2D space, poses which are similar (low RMSD) will appear closer together and poses which are dissimilar (high RMSD) will appear farther away.