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\item Compute the distances matrix between the trajectories   \item Use a general clustering algorithm which is independent of the metric used  \end{itemize}  Hence we used the clustering algorithm described in [?] \cite{NIPS2008_3478}  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.