Clustering is an unsupervised exploratory task that helps experts understanding the structure of their data. Constraints based on their knowledge can be introduced, but obtaining them remains challenging, making the explanation of results essential for adjusting parameters and uncovering new insights. We address explainable clustering by modeling the data in two spaces: one for clustering and another for explanation. Our method ECS (Explainability-driven Cluster Selection) aims to produce a high-quality clustering while ensuring interpretability through patterns that cover most instances in a cluster and distinguish them from others. It relies on ensemble clustering and a new constraint programming model for selecting the clusters and their explanations.