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This paper advocates the exploration of the full state of recorded real-timestrategy (RTS) games, by human or robotic players, to discover how to reasonabout tactics and strategy. We present a dataset of StarCraft gamesencompassing the most of the games' state (not only player's orders). Weexplain one of the possible usages of this dataset by clustering armies ontheir compositions. This reduction of armies compositions to mixtures ofGaussian allow for strategic reasoning at the level of the components. Weevaluated this clustering method by predicting the outcomes of battles based onarmies compositions' mixtures components

A Dataset for StarCraft AI \& an Example of Armies Clustering