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:
S. Miguel A. Barreto;Andrés Pérez-Uribe
Affiliations:
Université de Lausanne, Hautes Etudes Commerciales, Institut des Systèmes d'Information and University of Applied Sciences of Western Switzerland, HEIG-VD, REDS and Corporación BIOT ...;University of Applied Sciences of Western Switzerland, HEIG-VD, REDS
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 5
Cited 2

Self-organizing maps

Self-organizing maps
Distance Matrix Based Clustering of the Self-Organizing Map

ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A Nonlinear Mapping for Data Structure Analysis

IEEE Transactions on Computers
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets

IEEE Transactions on Neural Networks
Clustering of the self-organizing map

IEEE Transactions on Neural Networks

Unsupervised feature selection and general pattern discovery using Self-Organizing Maps for gaining insights into the nature of seismic wavefields

Computers & Geosciences
Analysis of Andean blackberry (Rubus glaucus) production models obtained by means of artificial neural networks exploiting information collected by small-scale growers in Colombia and publicly available meteorological data

Computers and Electronics in Agriculture

Quantified Score

Hi-index	0.00

Visualization

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.