title: Automatic Selection of Partitioning Variables for Small Multiple Displays.
abstract: Effective small multiple displays are created by partitioning a visualization on variables that reveal interesting conditional structure in the data. We propose a method that automatically ranks partitioning variables, allowing analysts to focus on the most promising small multiple displays. Our approach is based on a randomized, non-parametric permutation test, which allows us to handle a wide range of quality measures for visual patterns defined on many different visualization types, while discounting spurious patterns. We demonstrate the effectiveness of our approach on scatterplots of real-world, multidimensional datasets.
authors.forename: Anushka
authors.initials: A
authors.lastname: Anand
authors.forename: Justin
authors.initials: J
authors.lastname: Talbot