In this work, we propose an original method for aggregating multiple clustering coming from different sources of information. Each partition is encoded by a co-membership matrix between observations. Our approach uses a mixture of Stochastic Block Models (SBM) to group co-membership matrices with similar information into components and to partition observations into different clusters, taking into account their specificities within the components. The parameters are estimated using a Variational Bayesian EM algorithm. The Bayesian framework allows for selecting an optimal numbers of clusters and components.