<title>Approximating scatterplots of large datasets using distribution splats</title>

Many situations exist where the plotting of large data sets with categorical attributes is desired in a 3D coordinate system. For example, a marketing company may conduct a survey involving one million subjects and then plot peoples favorite car type against their weight, height and annual income. Scatter point plotting, in which each point is individually plotted at its correspond cartesian location using a defined primitive, is usually used to render a plot of this type. If the dependent variable is continuous, we can discretize the 3D space into bins or voxels and retain the average value of all records falling within each voxel. Previous work employed volume rendering techniques, in particular, splatting, to represent this aggregated data, by mapping each average value to a representative color.