Clutter affects almost any kind of visual technique and can obscure the structure present in the data even in small datasets, making it hard for users to find patterns and reveal relationships. In this paper we present a general strategy to analyze and reduce clutter using a special kind of sampling, together with an ad-hoc displacement technique and perceptual issues collected through a user study. The method, defined for 2D scatter plots, is flexible enough to be used in quite different contexts. In particular, in this paper we prove its usefulness against scatter plot, radviz, and parallel coordinates visualizations.
Density differences are one of the main features users perceive in 2D scatter plots. However, because of pixels’ collisions, some areas become saturated and such differences are lost. To solve this problem, several proposals rely on sampling the dataset before visualizing it. Some of these introduce precise measures to understand the image degradation and use numerical differences in pixels to estimate density differences. It is our opinion that this issue deserves a deeper analysis, taking into account perceptual issues. In this paper we describe a study we conducted to understand the relationship between numerical pixel density and the perceived density. The results obtained were used to refine a sampling technique we developed to preserve relative densities in the context of 2D scatter plots.