Ensemble clustering has emerged as an important elaboration of the classical clustering problems. Ensemble clustering refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better fit in some sense than the existing clusterings. Many approaches have been developed to solve ensemble clustering problems over the last few years. However, most of these ensemble techniques are designed for partitional clustering methods. Few research efforts have been reported for ensemble hierarchical clustering methods. In this paper, we propose a hierarchical ensemble clustering framework which can naturally combine both partitional clustering and hierarchical clustering results. We notice the importance of ultra-metric distance for hierarchical clustering and propose a novel method for learning the ultra-metric distance from the aggregated distance matrices and generating final hierarchical clustering with enhanced cluster separation. Experimental results demonstrate the effectiveness of our proposed approaches.