Recent advances in technology and the widespread use of tracking global positioning systems, such as GPS and RFID, and mobile technologies have made the access to spatio-temporal datasets increase at an accelerated pace. This large amount of data has led to develop efficient techniques to process queries about the behavior of moving objects, like the discovering of patterns among trajectories in a continuous period of time. Several studies have focused on the query of patterns capturing the behavior of moving objects reflected in collaborations such as mobile clusters, convoy queries and flock patterns. In this paper, a comparison between two algorithms for flocking, Basic Flock Evaluation (BFE) and LCMFLOCK, is presented in order to measure their performance and behavior in different datasets, both synthetic and real. This research is the first step towards proposing new algorithms in order to improve the drawbacks reported by the former methods.