  We used the superconductors in the SuperCon database to construct element
vectors and then perform unsupervised learning of their critical temperatures
(T$_c$). Only the chemical composition of superconductors was used in this
procedure. No physical predictors (neither experimental nor computational) of
any kind were used. We achieved the coefficient of determination
R$^2$$\simeq$0.93, which is comparable and in some cases higher then similar
estimates using other artificial intelligence techniques. Based on this machine
learning model, we predicted several new superconductors with high critical
temperatures. We also discuss the factors that limit the learning process and
suggest possible ways to overcome them.
