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\item Use a general clustering algorithm which is independent of the metric used  \end{itemize}  Hence we used the clustering algorithm described in [?] which take a distance matrix as single input. Furthermore it automatically selects the clusters' centers and the number of clusters.  Our contribution consist of a relevant similarity measure between trajectories based on Mutual Information  which allows the clustering of the players' trajectories according to the interdependency of their path. Yet only discrete trajectories are available in the form of array which can be of different sizes with different time discretization or with different speed.