SOMson -- Sonification of Multidimensional Data in Kohonen Maps
Simon Linke Tim Ziemer
Kohonen Maps, aka. Self-organizing maps (SOMs) are neural networks that
visualize a high-dimensional feature space on a low-dimensional map. While SOMs
are an excellent tool for data examination and exploration, they inherently
cause a loss of detail. Visualizations of the underlying data do not integrate
well and, therefore, fail to provide an overall picture. Consequently, we
suggest SOMson, an interactive sonification of the underlying data, as a data
augmentation technique. The sonification increases the amount of information
provided simultaneously by the SOM. Instead of a user study, we present an
interactive online example, so readers can explore SOMson themselves. Its
strengths, weaknesses, and prospects are discussed.