With technological progress we encounter more available data on the locations of moving objects and therefore the need for mining moving objects data is constantly growing. Mining spatio-temporal data can direct products and services to the right customers at the right time; it can also be used for resources optimization or for understanding mobile patterns. In this chapter, we cluster trajectories in order to find movement patterns of mobile objects. We use a compact representation of a mobile trajectory, which is based on a list of minimal bounding boxes (MBBs). We introduce a new similarity measure between mobile trajectories and compare it empirically to an existing similarity measure by clustering spatio-temporal data and evaluating the quality of resulting clusters and the algorithm run times.