We propose an efficient method to explore the configuration space of nanoclusters by combining together ab initio molecular dynamics, metadynamics, and data clustering algorithms. On the one side, we employ collective variables sensitive to topological changes in the network of interatomic connections to map the configuration space; on the other side, we introduce an automatic approach to select, in such a space, representative structures to be optimized. In this way, we show that it is possible to sample thoroughly the set of relevant nanocluster geometries at a limited computational cost. We apply our method to explore MoS2 clusters that recently raised a sizable interest due to their remarkable electronic and catalytic properties. We demonstrate that the unsupervised algorithm is able to find a large number of low-energy structures at different cluster sizes, including both bulk-like geometries and very different topologies. We are thus able to recapitulate, in a single computational study on technologically relevant MoS2 clusters, the results of all previous works that employed distinct techniques like genetic algorithms or heuristic hypotheses. Furthermore, we found several new structures not previously reported. The ensemble of MoS2 cluster structures is deposited in a publicly accessible database.