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The selection of weak classifiers is critical to the success of boosting techniques. Poor weak classifiers do not perform better than random guess, thus cannot help decrease the training error during the boosting process. Therefore, when constructing the weak classifier pool, we prefer the quality rather than the quantity of the weak classifiers. In this paper, we present a data mining-driven approach to discovering compositional features from a given and possibly small feature pool. Compared with individual features (e.g. weak decision stumps) which are of limited discriminative ability, the mined compositional features have guaranteed power in terms of the descriptive and discriminative abilities, as well as bounded training error. To cope with the combinatorial cost of discovering compositional features, we apply data mining methods (frequent itemset mining) to efficiently find qualified compositional features of any possible order. These weak classifiers are further combined through a multi-class AdaBoost method for final multi-class classification. Experiments on a challenging 10-class event recognition problem show that boosting compositional features can lead to faster decrease of training error and significantly higher accuracy compared to conventional boosting decision stumps.