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

The study objects were 48 microhabitats of five Utricularia 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 Utricularia 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 Utricularia species microhabitats in respect of water quality, from eutrophic for U. vulgaris to dystrophic for U. minor and U. intermedia. The used methods give similar results and constitute a validation of the SOFM method in this type of studies.

Download Full-text

The Rapid Kernel Classifier: A Link between the Self-Organizing Feature Map and the Radial Basis Function Network

Journal of Intelligent Material Systems and Structures ◽

10.1177/1045389x9400500207 ◽

1994 ◽

Vol 5 (2) ◽

pp. 211-219 ◽

Cited By ~ 3

Author(s):

Joydeep Ghosh ◽

Srinivasa V. Chakravarthy

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Radial Basis Function Network ◽

The Self ◽

Feature Map ◽

Radial Basis ◽

Function Network ◽

Self Organizing

Download Full-text

An efficient initialization scheme for the self-organizing feature map algorithm

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) ◽

10.1109/ijcnn.1999.832672 ◽

2003 ◽

Cited By ~ 6

Author(s):

Mu-Chun Su ◽

Ta-Kang Liu ◽

Hsiao-Te Chang

Keyword(s):

The Self ◽

Feature Map ◽

Map Algorithm ◽

Initialization Scheme ◽

Self Organizing

Download Full-text

SPHERICAL SELF-ORGANIZING FEATURE MAP: AN INTRODUCTORY REVIEW

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127406016732 ◽

2006 ◽

Vol 16 (11) ◽

pp. 3195-3206 ◽

Cited By ~ 13

Author(s):

ARCHANA P. SANGOLE ◽

ALEXANDROS LEONTITSIS

Keyword(s):

Fractal Dimension ◽

Chaotic Maps ◽

High Dimensional Data ◽

The Self ◽

High Dimensional ◽

Feature Map ◽

Engineering Sciences ◽

Physical Measures ◽

Introductory Review ◽

Self Organizing

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.

Download Full-text

Implementation of the self-organizing feature map on parallel computers

Parallel Processing: CONPAR 92—VAPP V - Lecture Notes in Computer Science ◽

10.1007/3-540-55895-0_483 ◽

1992 ◽

pp. 775-776 ◽

Cited By ~ 5

Author(s):

V. Demian ◽

J. -C. Mignot

Keyword(s):

Parallel Computers ◽

The Self ◽

Feature Map ◽

Self Organizing

Download Full-text

A parallel implementation of the self-organizing feature map using synchronous communication

10.1109/iscas.1990.112186 ◽

2002 ◽

Author(s):

R.E. Hodges ◽

C.-H. Wu ◽

C.-J. Wang

Keyword(s):

Parallel Implementation ◽

The Self ◽

Synchronous Communication ◽

Feature Map ◽

Self Organizing

Download Full-text

Clustering Urban Multifunctional Landscapes Using the Self-Organizing Feature Map Neural Network Model

Journal of Urban Planning and Development ◽

10.1061/(asce)up.1943-5444.0000170 ◽

2014 ◽

Vol 140 (2) ◽

pp. 05014001 ◽

Cited By ~ 9

Author(s):

Yang Gao ◽

Zhe Feng ◽

Yang Wang ◽

Jin-Long Liu ◽

Shuang-Cheng Li ◽

...

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

The Self ◽

Feature Map ◽

Multifunctional Landscapes ◽

Self Organizing

Download Full-text

Academic co-author networks based on the self-organizing feature map

2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) ◽

10.1109/fskd.2014.6980858 ◽

2014 ◽

Author(s):

Gege Zhang ◽

Weixing Zhou ◽

Yuanyuan Zhang ◽

Xiaohui Hu ◽

Yun Xue ◽

...

Keyword(s):

The Self ◽

Feature Map ◽

Self Organizing

Download Full-text

Self-Organizing Tree Using Artificial Ants

Interdisciplinary Advances in Information Technology Research ◽

10.4018/978-1-4666-3625-5.ch005 ◽

2013 ◽

pp. 60-74

Author(s):

Hanene Azzag ◽

Mustapha Lebbah

Keyword(s):

Self Assembly ◽

The Self ◽

Hierarchical Partitioning ◽

Hierarchical Tree ◽

New Approach ◽

Hierarchical Relations ◽

Artificial Ants ◽

Inherent Structure ◽

Assembly Behavior ◽

Self Organizing

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.

Download Full-text