We introduce a method to derive epsilon-nets of finite sets. It operates in a reproducing kernel Hilbert space. Its principle combines two well-known tools of empirical inference: the hierarchical agglomerative clustering and the computation of minimum enclosing balls. It produces epsilon-nets whose cardinalities are smaller than those obtained with state-of-the-art methods.