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Published in:
2007 | OriginalPaper | Chapter
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
Authors : Miguel A. Barreto S., Andrés Pérez-Uribe
Published in: Artificial Neural Networks – ICANN 2007
Publisher: Springer Berlin Heidelberg
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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.