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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 [?] which only take a distance matrix as input. Furthermore it automatically selected the clusters' center and the number of clusters.  Our contribution consist of a relevant similarity measure between trajectories which allows the clustering of the players' trajectories according to the interdependency of their path using clustering. 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. Then the clustering of those trajectories require a proper metric so as to cluster the players' trajectories.