In this work we present a method for clustering large unordered sets of cameras. Our method uses camera view information available from Structure-from-Motion (SfM) for computing a set of overlapping clusters suited for Multi-View Stereo (MVS) reconstruction. Our formulation of the problem uses the game theoretic model of dominant sets to find competing clustering solutions with computational simplicity. The overlapping solutions ensure more robust partial reconstructions. Experimental evaluations show that our method produces more regular cluster and overlap configurations with respect to the state of the art. This allows more scalable and higher quality reconstructions, while speeding up 6 times with respect to a MVS which uses all images at once.