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An algorithm for combining results of different clusterings is presented in this paper, the objective of which is to find groups of patterns which are common to all clusterings. The idea of the proposed combination is to group those samples which are in the same cluster in most cases. We formulate the combination as the resolution of a linear set of equations with binary constraints. The advantage of such a formulation is to provide an objective function for the combination. To optimize the objective function we propose an original unsupervised algorithm. Furthermore, we propose an extension adapted in case of a huge volume of data. The combination of clusterings is performed on the results of different clustering algorithms applied to SPOT5 satellite images and shows the effectiveness of the proposed method.