Clustering is fundamental to understand the structure of data. In the past decade the cluster ensemble problem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a single consensus solution that outperforms all the ensemble members. Although disagreement among ensemble partitions (diversity) has been found to be fundamental for success, the literature has arrived to confusing conclusions: some authors suggest that high diversity is beneficial for the final performance, whereas others have indicated that medium is better. While there are several options to measure the diversity, there is no method to control it. This paper introduces a new ensemble generation strategy and a method to smoothly change the ensemble diversity. Experimental results on three datasets suggest that this is an important step towards a more systematic approach to analyze the impact of the ensemble diversity on the overall consensus performance.Sociedad Argentina de Informática e Investigación Operativa