User-study Based Optimization of Fast and Accurate Mahalanobis Brushing in Scatterplots

Abstract

Brushing is at the heart of most modern visual analytics solutions with coordinated, multiple views and effective brushing is crucial for swift and efficient processes in data exploration and analysis. Given a certain data subset that the user wishes to brush in a data visualization, traditional brushes are usually either accurate (like the lasso) or fast (e.g., a simple geometry like a rectangle or circle). In this paper, we now present a new, fast and accurate brushing technique for scatterplots, based on the Mahalanobis brush, which we have extended and then optimized using data from a user study. We explain the principal, sketchbased model of our new brushing technique (based on a simple click-and-drag interaction), the details of the user study and the related parameter optimization, as well as a quantitative evaluation, considering efficiency, accuracy, and also a comparison with the original Mahalanobis brush.