Diffusion MR data sets produce large numbers of streamlines whichare hard to visualize, interact with, and interpret in a clinicallyacceptable time scale, despite numerous proposed approaches. As asolution we present a simple, compact, tailor-made clustering algorithm,QuickBundles (QB), that overcomes the complexity of these large datasets and provides informative clusters in seconds. Each QB cluster canbe represented by a single centroid streamline; collectively thesecentroid streamlines can be taken as an effective representation of thetractography. We provide a number of tests to show how the QB reductionhas good consistency and robustness. We show how the QB reduction canhelp in the search for similarities across several subjects.