scholarly journals Advanced Visualization Techniques for Self-organizing Maps with Graph-Based Methods

2005 ◽  
pp. 75-80 ◽  
Author(s):  
Georg Pölzlbauer ◽  
Andreas Rauber ◽  
Michael Dittenbach
Keyword(s):  
Self Organizing Maps ◽  
Visualization Techniques ◽  
Advanced Visualization ◽  
Self Organizing

Supervision of Industrial Processes using Self Organizing Maps

2012 ◽  
pp. 206-227 ◽  
Author(s):  
Ignacio Díaz ◽  
Abel A. Cuadrado ◽  
Alberto B. Diez ◽  
Manuel Domínguez ◽  
Juan J. Fuertes ◽  
...  
Keyword(s):  
Dynamical Behavior ◽  
Special Focus ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Seamless Integration ◽  
New Knowledge ◽  
Recent Developments ◽  
Visualization Techniques ◽  
Process Supervision ◽  
Self Organizing

The objective of this chapter is to present, in a comprehensive and unified way, a corpus of data and knowledge visualization techniques based on the Self-Organizing Map (SOM). These techniques allow exploring the behavior of the process in a visual and intuitive way through the integration of existing process-related knowledge with information extracted from data, providing new ways for knowledge discovery. With a special focus on the application to process supervision and modeling, the chapter reviews well known techniques –such as component planes, u-matrix, and projection of the process state– but also presents recent developments for visualizing process-related knowledge, such as fuzzy maps, local correlation maps, and model maps. It also introduces the maps of dynamics, which allow users to visualize the dynamical behavior of the process on a local model basis, in a seamless integration with the former visualizations, making it possible to confront all them for discovery of new knowledge.

scholarly journals Advanced visualization of Self-Organizing Maps with vector fields

2006 ◽  
Vol 19 (6-7) ◽  
pp. 911-922 ◽  
Author(s):  
Georg Pölzlbauer ◽  
Michael Dittenbach ◽  
Andreas Rauber
Keyword(s):  
Vector Fields ◽  
Self Organizing Maps ◽  
Advanced Visualization ◽  
Self Organizing

High-resolution Self-Organizing Maps for advanced visualization and dimension reduction

2018 ◽  
Vol 105 ◽  
pp. 166-184 ◽  
Author(s):  
Ayu Saraswati ◽  
Van Tuc Nguyen ◽  
Markus Hagenbuchner ◽  
Ah Chung Tsoi
Keyword(s):  
High Resolution ◽  
Dimension Reduction ◽  
Self Organizing Maps ◽  
Advanced Visualization ◽  
Self Organizing

A Psychographic Segmentation of Kuwaiti Travelers Using Self-Organizing Maps

2019 ◽  
Vol 24 (1) ◽  
pp. 87-92 ◽  
Author(s):  
Yvette Reisinger ◽  
Mohamed M. Mostafa ◽  
John P. Hayes
Keyword(s):  
Self Organizing Maps ◽  
Self Organizing

Self Organizing Maps, Pattern Recognition and Financial Crises

2011 ◽  
Author(s):  
Sylvain Barthelemy ◽  
Pascal Devaux ◽  
Francois Faure ◽  
Matthieu Pautonnier
Keyword(s):  
Pattern Recognition ◽  
Financial Crises ◽  
Self Organizing Maps ◽  
Self Organizing

Using self organizing maps to analyze larval fish assemblage vertical dynamics through environmental-ontogenetic gradients

2021 ◽  
pp. 107410
Author(s):  
I. Álvarez ◽  
J.S. Font-Muñoz ◽  
I. Hernández-Carrasco ◽  
C. Díaz-Gil ◽  
P.M. Salgado-Hernanz ◽  
...  
Keyword(s):  
Fish Assemblage ◽  
Larval Fish ◽  
Self Organizing Maps ◽  
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.

scholarly journals Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adeoluwa Akande ◽  
Ana Cristina Costa ◽  
Jorge Mateu ◽  
Roberto Henriques
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Northern Region ◽  
Extreme Weather Events ◽  
Self Organizing Map ◽  
Spatial And Temporal Patterns ◽  
Self Organizing Maps ◽  
The North ◽  
Precipitation Regimes ◽  
Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

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