Unsupervised Visual Data Mining Using Self-organizing Maps and a Data-driven Color Mapping

Abstract

This paper presents a new approach for visually mining multivariate datasets and especially large ones. This unsupervised approach proposes to mix a SOM approach and a pixel-oriented visualization. The map is considered as a set of connected pixels, the space filling is driven by the SOM algorithm, and the color of each pixel is computed directly from data using an approach proposed by Blanchard et al. The method visually summarizes the data and helps in understanding its inner structure.