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To enhance the generalization capacity of a distribution learning method, we propose to use a fuzzy Bayesian framework based on Bayes rules. The precision of the learning results is increased and the prediction quality is enhanced. The distribution learning method uses the information contained in the simulations and the knowledge of the measurements to learn a relation function. The fuzzy bayesian clustering (FBC) algorithm is a preprocessing technique that divides the whole learning space into subspaces where the generalization is better than the generalization into the whole space. We apply the FBC to a prediction tool of a third generation (3G) cellular radio network and results show that the generalization capacity is enhanced compared to classical clustering algorithms. Unobserved configuration can then be predicted with enhanced accuracy.