Cluster ensemble, as one of the important research directions in the ensemble learning area, is gaining more and more attention, due to its powerful capability to integrate multiple clustering solutions and provide a more accurate, stable and robust result. Cluster ensemble has a lot of useful applications in a large number of areas. Although most of traditional cluster ensemble approaches obtain good results, few of them consider how to achieve good performance for noisy datasets. Some noisy datasets have a number of noisy attributes which may degrade the performance of conventional cluster ensemble approaches. Some noisy datasets which contain noisy samples will affect the final results. Other noisy datasets may be sensitive to distance functions.