On Sketch-Based Selections From Scatterplots Using KDE, Compared to Mahalanobis and CNN Brushing

Fast and accurate brushing is crucial in visual data exploration and sketch-based solutions are successful methods. In this article, we detail a solution, based on kernel density estimation, which computes a data subset selection in a scatterplot from a simple click-and-drag interaction. We explain how this technique relates to two alternative approaches, i.e., Mahalanobis brushing and CNN brushing. To study this relation, we conducted two user studies and present both a quantitative three-fold comparison as well as additional details about the prevalence of all possible cases in that each technique succeeds/fails. With this, we also provide a comparison between empirical modeling and implicit modeling by DL in terms of accuracy, efficiency, generality, and interpretability.