A visualization technique for self-organizing maps with vector fields to obtain the cluster structure at desired levels of detail
Georg Pölzlbauer, Michael Dittenbach, Andreas Rauber · Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. · 2006
Self-organizing maps (SOMs) are a prominent tool for exploratory data analysis. One core task within the utilization of SOMs is the identification of the cluster structure on the map for which several visualization methods have been proposed, yet different application domains may require additional representation of the cluster structure. In this paper, we propose such a method based on pairwise distance calculation. It can be plotted on top of the map lattice with arrows that point to the closest cluster center. A parameter is provided that determines the granularity of the clustering. We provide experimental results and discuss the general applicability of our method, along with a comparison to related techniques.