SOMson -- Sonification of Multidimensional Data in Kohonen Maps
This is an incremental improvement for data analysts using self-organizing maps to enhance exploration capabilities.
The paper tackles the problem of detail loss in Kohonen maps by introducing SOMson, an interactive sonification technique that augments data visualization to provide more simultaneous information, with an online example for exploration.
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.