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

Author

Zhang, Jin-Tun ; Sun, Bo ; Ru, Wenming

Author_Institution

Coll. of Life Sci., Beijing Normal Univ., Beijing, China

fYear

2009

fDate

11-13 June 2009

Firstpage

1

Lastpage

4

Abstract

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.

Keywords

ecology; geophysical signal processing; pattern classification; pattern clustering; self-organising feature maps; vegetation; vegetation mapping; China; Guancen Mountains; artificial neural network; ecology; pattern classification; self-organizing feature map clustering; vegetation associations; woodland communities; Artificial neural networks; Ecosystems; Electronic mail; Environmental factors; Mathematics; Neural networks; Neurons; Soil; Temperature; Vegetation mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Beijing

Print_ISBN

978-1-4244-2901-1

Electronic_ISBN

978-1-4244-2902-8

Type

conf

DOI

10.1109/ICBBE.2009.5162395

Filename

5162395