scholarly journals Water trophicity of Utricularia microhabitats identlfied by means of SOFM as a tool in ecological modeling

2011 ◽  
Vol 76 (3) ◽  
pp. 255-261 ◽  
Author(s):  
Piotr Kosiba ◽  
Andrzej Stankiewicz

The study objects were 48 microhabitats of five <em>Utricularia</em> species in Lower and Upper Silesia (POLAND). The aim of the paper was to focus on application of the Self-Organizing Feature Map in assessment of water trophicity in <em>Utricularia</em> microhabitats, and to describe how SOFM can be used for the study of ecological subjects. This method was compared with the hierarchical tree plot of cluster analysis to check whether this techniques give similar results. In effect, both topological map of SOFM and dendrogram of cluster analysis show differences between <em>Utricularia</em> species microhabitats in respect of water quality, from eutrophic for <em>U. vulgaris</em> to dystrophic for <em>U. minor</em> and <em>U. intermedia</em>. The used methods give similar results and constitute a validation of the SOFM method in this type of studies.

2006 ◽  
Vol 16 (11) ◽  
pp. 3195-3206 ◽  
Author(s):  
ARCHANA P. SANGOLE ◽  
ALEXANDROS LEONTITSIS

The self-organizing feature map (SOFM) has received great attention from researchers in a variety of areas such as engineering sciences, medicine, biology and economics. The topology of these maps is usually based on 1, 2, or 3 dimensions, forming a lattice. This article discusses various aspects of the spherical SOFMs along with examples illustrating its implementation on high-dimensional data. The main advantage of the spherical SOFM is the ability to visualize complex high-dimensional data by encapsulating physical measures of the data within the 3D attributes of its spherical lattice. The article presents the potential of the spherical SOFM to visualize nonlinear data using examples of two chaotic maps, Hénon and Ikeda, with a fractal dimension of 1.2 and 1.7 respectively embedded in 2–5 dimensions.


2014 ◽  
Vol 140 (2) ◽  
pp. 05014001 ◽  
Author(s):  
Yang Gao ◽  
Zhe Feng ◽  
Yang Wang ◽  
Jin-Long Liu ◽  
Shuang-Cheng Li ◽  
...  

Author(s):  
Gege Zhang ◽  
Weixing Zhou ◽  
Yuanyuan Zhang ◽  
Xiaohui Hu ◽  
Yun Xue ◽  
...  
Keyword(s):  
The Self ◽  

Author(s):  
Hanene Azzag ◽  
Mustapha Lebbah

In this paper, the authors propose a new approach for topological hierarchical tree clustering inspired from the self-assembly behavior of artificial ants. The method, called SoTree (Self-organizing Tree), builds, autonomously and simultaneously, a topological and hierarchical partitioning of data. Each ’’cluster’’ associated to one cell of a 2D grid is modeled by a tree. The artificial ants similarly build a tree where each ant represents a node/data. The benefit of this approach is the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.


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