Neural Networks
The Continuous Interpolating Self-organizing Map
Neural Processing Letters
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Gradient estimation in global optimization algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
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The SOM-NG algorithm is a combination of the Self-Organizing Map and the Neural Gas algorithms. It was developed to combine quantization and topological preservation. The algorithm also has a supervised version to create local linear models of scalar fields in the defined Voronoi regions. In this work a new methodology is proposed to use those models as data mining tools. Using visual tools, gradients are analysed to discover the influence of each variable over the output. It does not only allow to select the most relevant variables but also to detect different zones of influence, which can be used to create a set of fuzzy rules. The proposed methodology is proven to be useful to detect locally relevant variables that lead to a better understanding of the data.