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.

翻译：Kohonen映射，亦称自组织映射（SOM），是一种将高维特征空间可视化于低维映射的神经网络。尽管SOM是数据检查与探索的卓越工具，其本质会导致细节信息的丢失。底层数据的可视化难以有效整合，因而无法提供整体概览。为此，我们提出SOMson——一种基于底层数据的交互式可听化技术，作为数据增强方法。该可听化技术同步提升了SOM所能提供的信息量。我们未采用用户研究，而是展示了一个交互式在线示例，以便读者能自行探索SOMson。文中讨论了该技术的优势、局限性与应用前景。