Multi-agent based simulations (MABS) have been successfully used to model complex systems in different areas. Nevertheless a pitfall of MABS is that their complexity increases with the number of agents and the number of different types of behavior considered in the model. For average and large systems, it is impossible to validate the trajectories of single agents in a simulation. The classical validation approaches, where only global indicators are evaluated, are too simplistic to give enough confidence in the simulation. It is then necessary to introduce intermediate levels of validation. In this paper we propose the use of data clustering and automated characterization of clusters in order to build, describe and follow the evolution of groups of agents in simulations. These tools provides the modeler with an intermediate point of view on the evolution of the model. Those tools are flexible enough to allow the modeler to define the groups level of abstraction (i.e. the distance between the groups level and the agents level) and the underlying hypotheses of groups formation. We give an online application on a simple NetLogo library model (Bank Reserves) and an offline log application on a more complex Economic Market Simulation.