scholarly journals A Comparative Study of the Cluster Analysis and the Self-Organizing Map in Spatial Analysis

2009 ◽  
Vol 26 (0) ◽  
pp. 77-84 ◽  
Author(s):  
Yan LI
Keyword(s):  
Cluster Analysis ◽  
Spatial Analysis ◽  
Comparative Study ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing

TreeSOM: Cluster analysis in the self-organizing map

2006 ◽  
Vol 19 (6-7) ◽  
pp. 935-949 ◽  
Author(s):  
Elena V. Samsonova ◽  
Joost N. Kok ◽  
Ad P. IJzerman
Keyword(s):  
Cluster Analysis ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

VISUAL APPROACH TO SUPERVISED VARIABLE SELECTION BY SELF-ORGANIZING MAP

2005 ◽  
Vol 15 (01n02) ◽  
pp. 101-110 ◽  
Author(s):  
TIMO SIMILÄ ◽  
SAMPSA LAINE
Keyword(s):  
Variable Selection ◽  
The Self ◽  
Data Sets ◽  
Self Organizing Map ◽  
Robust Method ◽  
Relevant Variables ◽  
Visual Approach ◽  
Predefined Criterion ◽  
Target Data ◽  
Self Organizing

Practical data analysis often encounters data sets with both relevant and useless variables. Supervised variable selection is the task of selecting the relevant variables based on some predefined criterion. We propose a robust method for this task. The user manually selects a set of target variables and trains a Self-Organizing Map with these data. This sets a criterion to variable selection and is an illustrative description of the user's problem, even for multivariate target data. The user also defines another set of variables that are potentially related to the problem. Our method returns a subset of these variables, which best corresponds to the description provided by the Self-Organizing Map and, thus, agrees with the user's understanding about the problem. The method is conceptually simple and, based on experiments, allows an accessible approach to supervised variable selection.

Representation and Identification of Fault Conditions of an Anaesthesia System by Means of the Self-Organizing Map

ICANN ’94 ◽  
1994 ◽  
pp. 350-353 ◽  
Author(s):  
Mauri Vapola ◽  
Olli Simula ◽  
Teuvo Kohonen ◽  
Pekka Meriläinen
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals Robust classification with reject option using the self-organizing map

2015 ◽  
Vol 26 (7) ◽  
pp. 1603-1619 ◽  
Author(s):  
Ricardo Gamelas Sousa ◽  
Ajalmar R. Rocha Neto ◽  
Jaime S. Cardoso ◽  
Guilherme A. Barreto
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Reject Option ◽  
Robust Classification ◽  
Self Organizing
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