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

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3 Author(s)
Jin-Tun Zhang ; Coll. of Life Sci., Beijing Normal Univ., Beijing, China ; Bo Sun ; Wenming Ru

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.

Published in:
Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on

Date of Conference: 11-13 June 2009

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