Parallel Sets is a powerful technique for the interactive visual analysis of categorical data. In addition to visualizing the existing data, parallel sets allow the user to create new data dimensions by combining existing ones, thus tailoring the data to the analysis process and the user’s model. We are currently adding new capabilities, such as including the display of textual dimensions, and also building a Bayesian model of the data directly from the visualization. This will allow the user to explicitly create a knowledge representation of a data set, and then test this model against other data. Work on Parallel Sets is led by Robert Kosara.