Coloring that reveals high-dimensional structures in data

Introduces a method for assigning colors to displays of cluster structures of high-dimensional data, such that the perceptual differences of the colors reflect the distances in the original data space as faithfully as possible. The cluster structure is first discovered with a self-organizing map (SOM), and then a new nonlinear projection method is applied to map the cluster structure into the CIELab color space. The projection method preserves best the local data distances that are the most important ones, while the global order is still discernible from the colors, too. This allows the method to conform flexibly to the available color space. The output space of the projection need not necessarily be the color space, however. Projections onto, say, two dimensions can be visualized as well.