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The increasing demand for dealing with uncertainty in data has led to the development of effective and efficient approaches in the data management and mining contexts. Clustering uncertain data objects has particularly attracted great attention in the data mining community. Most existing clustering methods however have urgently to come up with a number of issues, some of which are related to a poor efficiency mainly due to an expensive computation of the distance between uncertain objects. In this work, we propose a novel formulation to the problem of clustering uncertain objects, which allows for reaching accurate solutions by minimizing the variance of the mixture models that represent the clusters to be identified. We define a heuristic, MMVar, which exploits some analytical properties about the computation of variance for mixture models to compute local minima of the objective function at the basis of the proposed formulation. This characteristic allows MMVar to discard any distance measure between uncertain objects and, therefore, to achieve high efficiency. Experiments have shown that MMVar outperforms state-of-the-art algorithms from an efficiency viewpoint, while achieving better average performance in terms of accuracy.