Self-Organizing Map

Ecological data are considered difficult to analyze because numerous biological and environmental factors are involved in ecological processes in a complex manner. The self-organizing map (SOM) has been an efficient alternative tool for analyzing ecological data without a priori knowledge. The unsupervised learning process was applied to provide a comprehensive view on ecological data through the use of ordination and classification. The SOM extracts information from multidimensional data and maps it onto two- or three-dimensional space. The network structure and learning algorithm are discussed to reveal the adaptive convergence of connection weights among computation nodes (i.e., neurons). Examples are provided to demonstrate the environmental impact gradient and sample unit clustering. SOM visualization is also presented to show profiles of the corresponding taxa and environmental variables.