In the context of semi-supervised learning and clustering ensemble methods, we introduce a novel strategy to consider not only pairwise constraints, but also triplet constraints. As far as we are aware, the latter have not been addressed in the literature of semi-supervised clustering ensembles. The strategy consists of a post-processing applied once a consensus partition has been built. Taking into account the fact that the clusters of the consensus partition are usually not spherical, in order to maintain their complex shapes, we first generate anchors, which are data points judged representative of the clusters, in such a way that every point in the cluster has an anchor close to it. These anchors are then used to create a data structure that we call an allocation matrix, which measures the assignment score of each point to each cluster in the consensus partition. Such a matrix is provided to an Integer Linear Programing (ILP) model to find the partition which is closest to the consensus partition, while satisfying the constraints. The experimental results show that our method, given an initial consensus partition and a set of constraints, allows to satisfy all the given constraints, modifying the initial partition, but without deteriorating considerably its quality.