Integrating automatic genre analysis into digital libraries
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
TreeSOM: cluster analysis in the self-organizing map
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Visualising class distribution on self-organising maps
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Reliable hierarchical clustering with the self-organizing map
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Assisted descriptor selection based on visual comparative data analysis
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Opening up the "black box" of medical image segmentation with statistical shape models
The Visual Computer: International Journal of Computer Graphics
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The self-organizing map (SOM) is widely used as a data visualization method in various engineering applications. It performs a non-linear mapping from a high-dimensional data space to a lower dimensional visualization space. In this paper, a simple method for visualizing the cluster structure of SOM model vectors is presented. The method may be used to produce tree-like visualizations, but the main application here is to get different color codings that express the approximate cluster structure of the SOM model vectors. This coloring may be exploited in making false color (pseudo color) presentations of the original data. The method is especially meant for making an easily implementable, explorative cluster visualization tool.