The problem considered is cluster analysis with the usage of the ensemble approach. The paper proposes a method for finding optimal weights for the averaged co-association matrix applied to the construction of the ensemble partition. The main idea is to find such weights for which the expectation of ensemble margin takes its maximum value. A latent variable pairwise classification model is used for determining margin characteristics dependent on cluster validity indices. To construct the ensemble partition, we apply a minimum spanning tree found on the averaged co-association matrix as an adjacency matrix. The efficiency of the method is confirmed by Monte-Carlo simulations with artificial data sets.