In order to be useful, visualizations need to be interpretable. This paper uses a user-based 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.
|Title of host publication||NIPS Workshop on Interpretable Machine Learning in Complex Systems|
|Place of Publication||Barcelona|
|Publication status||Published - 2016|