One-class classification belongs to the one of the novel and very promising topics in contemporary machine learning. In recent years ensemble approaches have gained significant attention due to increasing robustness to unknown outliers and reducing the complexity of the learning process. In our previous works, we proposed a highly efficient one-class classifier ensemble, based on input data clustering and training weighted one-class classifiers on clustered subsets. However, the main drawback of this approach lied in difficult and time consuming selection of a number of competence areas which indirectly affects a number of members in the ensemble. In this paper, we investigate ten different methodologies for an automatic determination of the optimal number of competence areas for the proposed ensemble. They have roots in model selection for clustering, but can be also effectively applied to the classification task. In order to select the most useful technique, we investigate their performance in a number of one-class and multi-class problems. Numerous experimental results, backed-up with statistical testing, allows us to propose an efficient and fully automatic method for tuning the one-class clustering-based ensembles.