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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Barreto S., M. A., & Pérez-Uribe, A. (2007). Improving the correlation hunting in a large quantity of SOM component planes classification of agro-ecological variables related with productivity in the sugar cane culture. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4669 LNCS, pp. 379–388). Springer Verlag. https://doi.org/10.1007/978-3-540-74695-9_39
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