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

机译：自组织特征图聚类在林地群落分类中的应用

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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.

机译：人工神经网络在分析和解决复杂的非线性问题方面功能强大。描述了SOFM（自组织特征图）聚类并将其应用于中国冠岑山林地群落的分析。该数据集由53个样方中的112个物种的重要性值组成。 SOFM聚类将53个四方类分为八类，代表了八种植被。这些结果具有生态学意义，表明SOFM聚类是生态学研究的有效方法。

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《Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009》|2009年|1-4|共4页
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Jin-Tun Zhang; Bo Sun; Wenming Ru;
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ecology; geophysical signal processing; pattern classification; pattern clustering; self-organising feature maps; vegetation; vegetation mapping; China; Guancen Mountains; artificial neural network; self-organizing feature map clustering; vegetation associations; woodland communities;

机译：生态学;地球物理信号处理;模式分类;模式聚类;自组织特征图;植被;植被图;中国;关恩山;人工神经网络;自组织特征图聚类;植被协会;林地群落;

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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 [J] . Zhang J. -T., Li S., Li M. Community Ecology . 2010,第1期

机译：自组织特征图聚类与TWINSPAN和模糊C均值聚类在中国关岑山林地群落分析中的比较
2. FROM IMAGE PROCESSING TO CLASSIFICATION .2. CLASSIFICATION OF ELECTROPHORETIC PATTERNS USING SELF-ORGANIZING FEATURE MAPS AND FEED-FORWARD NEURAL NETWORKS [J] . Kesmir C., Jensen K., Sondergaard I. Electrophoresis: The Official Journal of the International Electrophoresis Society . 1995,第6期

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3. Cluster identification and separation in the growing self-organizing map: application in protein sequence classification [J] . Norashikin Ahmad, Damminda Alahakoon, Rowena Chau Neural computing & applications . 2010,第4期

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4. Application of Self-Organizing Feature Map clustering to the classification of woodland communities [C] . Jin-Tun Zhang, Bo Sun, Wenming Ru The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议）论文集 . 2009

机译：自组织特征图聚类在林地群落分类中的应用
5. Feature selection for self-organizing feature map neural networks with applications in medical image segmentation. [D] . Rickard, Heather Erin. 2001

机译：自组织特征图神经网络的特征选择及其在医学图像分割中的应用。
6. Application of Kohonens self-organizing feature map algorithm to cortical maps of orientation and direction preference [O] . N. V. Swindale, H.-U. Bauer 1998

机译：Kohonen的自组织特征图算法在取向和方向偏好的皮层图中的应用
7. Clustering and Classification based on Expert knowledge propagation using Probabilistic Self-Organizing Map: application to geophysics [O] . Méziane Yacoub, Dominique Frayssinet, Fouad Badran, 2008

机译：基于概率自组织映射的专家知识传播的聚类和分类：地球物理学应用
8. Pattern classification using self-organizing feature maps. [R] . Lu, S. 1990

机译：使用自组织特征映射进行模式分类。

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

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