Most clustering algorithms produce a single clustering solution. This is inadequate for many data sets that are multi-faceted and can be grouped and interpreted in many different ways. Moreover, for high-dimensional data, different features may be relevant or irrelevant to each clustering solution, suggesting the need for feature selection in clustering. Features relevant to one clustering interpretation may be different from the ones relevant for an alternative interpretation or view of the data. In this paper, we introduce a probabilistic nonparametric Bayesian model that can discover multiple clustering solutions from data and the feature subsets that are relevant for the clusters in each view. In our model, the features in different views may be shared and therefore the sets of relevant features are allowed to overlap. We model feature relevance to each view using an Indian Buffet Process and the cluster membership in each view using a Chinese Restaurant Process. We provide an inference approach to learn the latent parameters corresponding to this multiple partitioning problem. Our model not only learns the features and clusters in each view but also automatically learns the number of clusters, number of views and number of features in each view.