  In this paper we introduce the ROman COmmonware POTtery (ROCOPOT) database,
which comprises of more than 2000 black and white imaging profiles of pottery
shapes extracted from 11 Roman catalogues and related to different excavation
sites. The partiality and the handcrafted variance of the shape fragments
within this new database make their unsupervised clustering a very challenging
problem: profile similarities are thus explored via the hierarchical clustering
of non-linear features learned in the latent representation space of a stacked
sparse autoencoder (SSAE) network, unveiling new profile matches. Results are
commented both from a mathematical and archaeological perspective so as to
unlock new research directions in the respective communities.
