Clustering methods are the most used algorithms for unsupervised learning. However, there is no unique optimal approach for all datasets since different clustering algorithms produce different partitions. To overcome this issue of selecting an appropriate technique and its corresponding parameters, cluster ensemble strategies are used for improving accuracy and robustness by a weighted combination of two or more approaches. However, this process is often carried out almost in a blind manner, testing different combinations of methods and assessing if its performance is beneficial for the defined purpose. Thus, the procedure for selecting the best combination tests many clustering ensembles until the desired result is achieved. This paper proposes a novel analytic tool for clustering ensemble generation, based on quantitative metrics and interactive visual resources. Our approach allows the analysts to display different results from state-of-the-art clustering methods and analyze their performance based on specific metrics and visual inspection. Based on their requirements/experience, the analysts can interactively assign weights to the different methods to set their contributions and manage (create, store, compare, and merge), such as for ensembles. Our approach's effectiveness is shown through a set of experiments and case studies, attesting to its usefulness in practical applications.