The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data

2002 ◽  
Vol 13 (6) ◽  
pp. 1331-1341 ◽  
Author(s):  
A. Rauber ◽  
D. Merkl ◽  
M. Dittenbach
Keyword(s):  
High Dimensional Data ◽  
Exploratory Analysis ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Self Organizing

GPU Accelerated Self-Organizing Map for High Dimensional Data

2014 ◽  
Vol 41 (3) ◽  
pp. 341-355 ◽  
Author(s):  
Yi Xiao ◽  
Rui-Bin Feng ◽  
Zi-Fa Han ◽  
Chi-Sing Leung
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Self Organizing

A SOM PROJECTION TECHNIQUE WITH THE GROWING STRUCTURE FOR VISUALIZING HIGH-DIMENSIONAL DATA

2003 ◽  
Vol 13 (05) ◽  
pp. 353-365 ◽  
Author(s):  
ZHENG WU ◽  
GARY G. YEN
Keyword(s):  
Projection Method ◽  
Cell Structure ◽  
High Dimensional Data ◽  
Network Size ◽  
High Dimensional ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Growing Cell ◽  
Self Organizing

The Self-Organizing Map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, an intuitive and effective SOM projection method is proposed for mapping high-dimensional data onto the two-dimensional grid structure with a growing self-organizing mechanism. In the learning phase, a growing SOM is trained and the growing cell structure is used as the baseline framework. In the ordination phase, the new projection method is used to map the input vector so that the input data is mapped to the structure of the SOM without having to plot the weight values, resulting in easy visualization of the data. The projection method is demonstrated on four different data sets, including a 118 patent data set and a 399 checical abstract data set related to polymer cements, with promising results and a significantly reduced network size.

scholarly journals An improved Kohonen self-organizing map clustering algorithm for high-dimensional data sets

2021 ◽  
Vol 24 (1) ◽  
pp. 600
Author(s):  
Momotaz Begum ◽  
Bimal Chandra Das ◽  
Md. Zakir Hossain ◽  
Antu Saha ◽  
Khaleda Akther Papry
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
High Dimensional Data ◽  
Predictive Performance ◽  
High Dimensional ◽  
Data Sets ◽  
Self Organizing Map ◽  
Distance Measurements ◽  
Cancer Data ◽  
Self Organizing

<p>Manipulating high-dimensional data is a major research challenge in the field of computer science in recent years. To classify this data, a lot of clustering algorithms have already been proposed. Kohonen self-organizing map (KSOM) is one of them. However, this algorithm has some drawbacks like overlapping clusters and non-linear separability problems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation formula. So far as we know, no previous work has used EISEN Cosine correlation distance measurements to classify high-dimensional data sets. To the robustness of the proposed KSOM, we carry out the experiments on several popular datasets like Iris, Seeds, Glass, Vertebral column, and Wisconsin breast cancer data sets. Our proposed algorithm shows better result compared to the existing original KSOM and another modified KSOM in terms of predictive performance with topographic and quantization error.</p>

Self-Organizing Map and Relational Perspective Mapping for the Accurate Visualization of High-Dimensional Hyperspectral Data

2020 ◽  
Vol 92 (15) ◽  
pp. 10450-10459 ◽  
Author(s):  
Wil Gardner ◽  
Ruqaya Maliki ◽  
Suzanne M. Cutts ◽  
Benjamin W. Muir ◽  
Davide Ballabio ◽  
...  
Keyword(s):  
Hyperspectral Data ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Relational Perspective ◽  
Self Organizing

SPHERICAL SELF-ORGANIZING FEATURE MAP: AN INTRODUCTORY REVIEW

2006 ◽  
Vol 16 (11) ◽  
pp. 3195-3206 ◽  
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.

Self-Organizing Map Learning Nonlinearly Embedded Manifolds

2005 ◽  
Vol 4 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Timo Similä
Keyword(s):  
Learning Algorithm ◽  
Image Data ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Complex Data ◽  
Dimensional Manifold ◽  
Self Organizing Map ◽  
Training Strategy ◽  
Low Dimensional ◽  
Self Organizing

One of the main tasks in exploratory data analysis is to create an appropriate representation for complex data. In this paper, the problem of creating a representation for observations lying on a low-dimensional manifold embedded in high-dimensional coordinates is considered. We propose a modification of the Self-organizing map (SOM) algorithm that is able to learn the manifold structure in the high-dimensional observation coordinates. Any manifold learning algorithm may be incorporated to the proposed training strategy to guide the map onto the manifold surface instead of becoming trapped in local minima. In this paper, the Locally linear embedding algorithm is adopted. We use the proposed method successfully on several data sets with manifold geometry including an illustrative example of a surface as well as image data. We also show with other experiments that the advantage of the method over the basic SOM is restricted to this specific type of data.

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