ACM Computing Surveys (CSUR)
Visual exploration of high-dimensional spatial data: requirements and deficits
Computers & Geosciences
On the use of self-organizing maps for clustering and visualization
Intelligent Data Analysis
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Using self-organizing maps to visualize high-dimensional data
Computers & Geosciences
The self-organizing map, the Geo-SOM, and relevant variants for geosciences
Computers & Geosciences
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Self-organizing maps as substitutes for k-means clustering
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
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The Self-Organizing Map (SOM) is an artificial neural network that performs simultaneously vector quantization and vector projection. Due to this characteristic, the SOM can be visualized through the output space, i.e. considering the vector projection perspective, and through the input data space, emphasizing the vector quantization process. Among all the strategies for visualizing the SOM, we will focus in those that allow dealing with spatial dependency, generally present in geo-referenced data. In this paper a method is presented for spatial clustering that integrates the visualization of both perspectives of a SOM: linking its output space, defined in up to three dimensions (3D), to the cartographic representation through a ordered set of colors; and exploring the use of border lines among geo-referenced elements, computed according to the distances in the input data space between their Best Matching Units. The promising results presented in this paper, focused on ecological modeling, urban modeling and climate analysis, show that the proposed method is a valuable tool for addressing a wide range of problems within the geosciences, especially when it is necessary to visualize high dimensional geo-referenced data.