Application of Self-Organizing Feature Map Clustering to the Classification of Woodland Communities

Artificial neural network is powerful in analyzing and solving complicated and non-linear matters. SOFM (self-organizing feature map) clustering was described and applied to the analysis of woodland communities in the Guancen Mountains of China. The dataset was consisted of importance values of 112 species in 53 quadrats. SOFM clustering classified the 53 quadrats into eight groups, representing eight associations of vegetation. These results are ecologically meaningful, which suggests that SOFM clustering is effective method in studies of ecology.