Community Ecology
Volume/Issue:
Volume 11: Issue 1
A comparison of self-organizing feature map clustering with TWINSPAN and fuzzy C-means clustering in the analysis of woodland communities in the Guancen Mts, China
Authors:
J. Zhang Beijing Normal University College of Life Sciences Beijing 100875 China

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,
S. Li Shanxi University Institute of Loess Plateau Taiyuan 030006 China

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M. Li Shanxi University Institute of Loess Plateau Taiyuan 030006 China

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Pages:
120–126
Online Publication Date:
17 Jun 2010
Publication Date:
01 Jun 2010
Article Category:
Research Article
DOI:
https://doi.org/10.1556/comec.11.2010.1.17
Keywords:
Artificial neural network; Classification; Plant community; Quantitative method; Vegetation-environment relation
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SOFM (self-organizing feature map) clustering is powerful in analyzing and solving complicated and non-linear problems. This method was used and compared with fuzzy C-means clustering and TWINSPAN, the most common classification methods, in analysis of plant communities in the Guancen Mts., China. The dataset consisted of importance values of 112 species in 53 quadrats of 10 m × 20 m. All the three methods classified the 53 quadrats into eight groups, representing eight associations of vegetation. They were all effective in the analysis of ecological data. The consistency of SOFM clustering with fuzzy C-means clustering (FCM) and TWINSPAN classification was 81.1% and 94.3%, respectively. SOFM clustering has some advantages and more potentiality in application to studies of ecology.

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Cover Community Ecology
Community Ecology An Interdisciplinary Journal Reporting Progress in Community and Population Studies
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Community Ecology
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