Improving the Correlation Hunting in a Large Quantity of SOM Component Planes

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.

[1]  David C. Sterratt,et al.  Does Morphology Influence Temporal Plasticity? , 2002, ICANN.

[2]  Young-Seuk Park,et al.  Patternizing communities by using an artificial neural network , 1996 .

[3]  Ruoying He,et al.  Sea Surface Temperature Patterns on the West Florida Shelf Using Growing Hierarchical Self-Organizing Maps , 2006 .

[4]  Jeanny Hérault,et al.  Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets , 1997, IEEE Trans. Neural Networks.

[5]  Juha Vesanto,et al.  Hunting for Correlations in Data Using the Self-Organizing Map , 1999 .

[6]  Sovan Lek,et al.  A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination , 2001 .

[7]  Igor Jurisica,et al.  Binary tree-structured vector quantization approach to clustering and visualizing microarray data , 2002, ISMB.

[8]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[9]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[10]  Paulo J. G. Lisboa,et al.  Segmentation of the on-line shopping market using neural networks , 1999 .

[11]  Juha Vesanto,et al.  Distance Matrix Based Clustering of the Self-Organizing Map , 2002, ICANN.

[12]  Hsin-Chung Lu,et al.  Prediction of daily maximum ozone concentrations from meteorological conditions using a two-stage neural network , 2006 .

[13]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[14]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.