  In this work, we address the unsupervised classification issue by exploiting
the general idea of Random Projection Ensemble. Specifically, we propose to
generate a set of low dimensional independent random projections and to perform
model-based clustering on each of them. The top $B^*$ projections, i.e. the
projections which show the best grouping structure are then retained. The final
partition is obtained by aggregating the clusters found in the projections via
consensus. The performances of the method are assessed on both real and
simulated datasets. The obtained results suggest that the proposal represents a
promising tool for high-dimensional clustering.

Methodology