Abstract
A technique called component planes is commonly used to visualize variables behavior with Self-Organizing Maps (SOMs). Nevertheless, when the component planes are too many the visualization becomes difficult. A methodology has been developed to enhance the component planes analysis process. This methodology improves the correlation hunting in the component planes with a tree-structured cluster representation based on the SOM distance matrix. The methodology presented here was used in the classification of similar agro-ecological variables and productivity in the sugar cane culture. Analyzing the obtained groups it was possible to extract new knowledge about the variables more related with the highest productivities.
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Barreto S., M.A., Pérez-Uribe, A. (2007). Improving the Correlation Hunting in a Large Quantity of SOM Component Planes. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_39
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DOI: https://doi.org/10.1007/978-3-540-74695-9_39
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