Self-Organizing Maps
RecMap: Rectangular Map Approximations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
IEEE Computer Graphics and Applications
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Magnification control for batch neural gas
Neurocomputing
Controlling the magnification factor of self-organizing feature maps
Neural Computation
Carto-SOM: cartogram creation using self-organizing maps
International Journal of Geographical Information Science
Explicit Magnification Control of Self-Organizing Maps for “Forbidden” Data
IEEE Transactions on Neural Networks
Asymptotic level density in topological feature maps
IEEE Transactions on Neural Networks
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This paper presents a simple way to compensate the magnification effect of Self-Organizing Maps (SOM) when creating cartograms using Carto-SOM. It starts with a brief explanation of what a cartogram is, how it can be used, and what sort of metrics can be used to assess its quality. The methodology for creating a cartogram with a SOM is then presented together with an explanation of how the magnification effect can be compensated in this case by pre-processing the data. Examples of cartograms produced with this method are given, concluding that Self-Organizing Maps can be used to produce high quality cartograms, even using only standard software implemen-tations of SOM.