Multivariate data analysis by means of self-organizing maps

Ecological data range widely in variability, showing non-linear and complex relationships among variables. Although conventional multivariate analyses are useful tools to explore ecological data, data mining by non-linear methods is preferred because a high degree of complexity resides in ecological phenomena. One of these methods is artificial neural networks in machine learning based on biologically inspired learning algorithms. Self-organizing map (SOM) is one of the most popular unsupervised artificial neural networks and are commonly used to seek patterns and clusters in ecological data. SOMs are versatile in analysing non-linear and complex data, which are observed frequently in ecological systems. In this paper, we explain the theory of SOMs and their application in ecological modelling, with a focus on learning processes, visualization, preprocessing of input data, and interpretation of results. We also discuss the advantages and disadvantages of SOM approaches.