Implements the methodology proposed by Anderlucci, Fortunato and Montanari (2019) for high-dimensional unsupervised classification. The random projection ensemble clustering algorithm applies a Gaussian Mixture Model to different random projections of the high-dimensional data and selects a subset of solutions accordingly to the Bayesian Information Criterion, computed here as discussed in Raftery and Dean (2006) . The clustering results obtained on the selected projections are then aggregated via consensus to derive the final partition.