We focus on the scenario of robust information clustering (RIC) based on the minimax optimization of mutual information (MI). The minimization of MI leads to the standard mass-constrained deterministic annealing clustering, which is an empirical risk-minimization algorithm. The maximization of MI works out an upper bound of the empirical risk via the identification of outliers (noisy data points). Furthermore, we estimate the real risk VC-bound and determine an optimal cluster number of the RIC based on the structural risk-minimization principle. One of the main advantages of the minimax optimization of MI is that it is a nonparametric approach, which identifies the outliers through the robust density estimate and forms a simple data clustering algorithm based on the square error of the Euclidean distance.