Graph projection techniques for Self-Organizing Maps
Georg Pölzlbauer, Andreas Rauber, Michael Dittenbach · 2005
Abstract. The Self-Organizing Map is a popular neural network model for data analysis, for which a wide variety of visualization techniques exists. We present two novel techniques that take the density of the data into account. Our methods define graphs resulting from nearest neighbor- and radius-based distance calculations in data space and show projections of these graph structures on the map. It can then be observed how relations between the data are preserved by the projection, yielding interesting insights into the topology of the mapping, and helping to identify outliers as well as dense regions. 1