The Self-Organizing Map in Industry Analysis

The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualization of high-dimensional data. It maps nonlinear statistical relationships between high-dimensional measurement data into simple geometric relationships, usually on a two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. The need for visualization and clustering occurs, for instance, in the data analysis of complex processes or systems. In various engineering applications, entire elds of industry can be investigated using SOM based methods. The data exploration tool presented in this chapter allows visualization and analysis of large data bases of industrial systems. Forest industry is the rst chosen application for the tool. To illustrate the global nature of forest indsutry, the example case is used to cluster the pulp and paper mills of the world.