Automatic Selection of Partitioning Variables for Small Multiple Displays

Description: 

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: 
Anushka Anand
Justin Talbot
Publication Date: 
Sunday, October 25, 2015
Publication Information: 
The IEEE Information Visualization Conference (Chicago, October 25-30, 2015)