Patternizing communities by using an artificial neural network

The Kohonen network, an unsupervised learning algorithm in artificial neural networks, performs self-organizing mapping and reduces dimensions of a complex data set. In this study, the network was applied to clustering and patternizing community data in ecology. The input data were benthic macroinvertebrates collected at study sites in the Suyong river in Korea. The grouping resulting from learning by the Kohonen network was comparable to the classification by conventional clustering methods. Through patternizing, the network showed a possibility of producing easily comprehensible low-dimensional maps under the total configuration of community groups in a target ecosystem. Changes in spatio-temporal community patterns may also be traced through the recognition process.