A single user activity, such as planning a conference trip, typically involves multiple actions. Although these actions may involve several applications, the central point of coordination for any particular activity is usually email. Previous work on email activity management has focused on clustering emails by activity. Dredze et al. [3] accomplished this by combining supervised classifiers based on document similarity, authors and recipients, and thread information. In this paper, we take a different approach and present an unsupervised framework for email activity clustering. We use the same information sources as Dredze et al.—namely, document similarity, message recipients and authors, and thread information—but combine them to form an unsupervised, non-parametric Bayesian user model. This approach enables email activities to be inferred without any user input. Inferring activities from a user’s mailbox adapts the model to that user. We next describe the statistical machinery that forms the basis of our user model, and explain how several email properties may be incorporated into the model. We evaluate this approach using the same data as Dredze et al., showing that our model does well at clustering emails by activity.