Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment

Benoît Frénay PReCISE Research Center Faculty of Computer Science University of Namur Namur, 5000 - Belgium benoit.frenay@unamur.be In order to be useful, visualizations need to be interpretable. This paper uses a userbased approach to combine and assess quality measures in order to better model user preferences. Results show that cluster separability measures are outperformed by a neighborhood conservation measure, even though the former are usually considered as intuitively representative of user motives. Moreover, combining measures, as opposed to using a single measure, further improves prediction performances.