2d projections of complex multidimensional data are unreliable in the extreme as to adjacency meaning. Most adjacency especially are an artifact of the chosen projection method.
[1] http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV09...
Somehow its technically challenging to verify the content of this article.
The original Sammon's paper is here [1], this said from what I know isomaps are a more widespread tool - but I never found such a good visualization.
[1] http://theoval.cmp.uea.ac.uk/~gcc/matlab/sammon/sammon.pdf
The S is for "stochastic" -- i.e. you get a different 2D projection every time you run it on the same inputs. Take it with a grain of salt.
That's not the part that's "stochastic"; sensitivity to initial conditions is just nonconvex optimization in action. You get the same thing with most other local embeddings.
The stochastic bit is that the model is based on optimizing "the asymmetric probability, pij , that i would pick j as its neighbor"[0]. Those probabilities and the associated positions in 2D space are not estimated stochastically (e.g. with Monte Carlo sampling) or anything, though.