Analysis and Improvements of the Pareto Optimal Solution Visualization Method Using the Self-Organizing Maps

2015 ◽  
Vol 8 (1) ◽  
pp. 34-43 ◽  
Author(s):  
Atsushi HIRONAKA ◽  
Takashi OKAMOTO ◽  
Seiichi KOAKUTSU ◽  
Hironori HIRATA
Keyword(s):  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
The Self ◽  
Pareto Optimal ◽  
Visualization Method ◽  
Self Organizing Maps ◽  
Self Organizing

A Pareto Optimal Solution Visualization Method Using an Improved Growing Hierarchical Self-Organizing Maps Based on the Batch Learning

2016 ◽  
Vol 20 (5) ◽  
pp. 691-704
Author(s):  
Naoto Suzuki ◽  
◽  
Takashi Okamoto ◽  
Seiichi Koakutsu
Keyword(s):  
Learning Algorithm ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Visualization Method ◽  
Self Organizing Maps ◽  
Optimal Solution Set ◽  
Batch Learning ◽  
Solution Representation ◽  
Self Organizing

In the multi-objective optimization problem that appears naturally in the decision making process for the complex system, the visualization of the innumerable solutions called Pareto optimal solutions is an important issue. This paper focuses on the Pareto optimal solution visualization method using the growing hierarchical self-organizing maps (GHSOM) which is one of promising visualization methods. This method has a superior Pareto optimal solution representation capability, compared to the visualization method using the self-organizing maps. However, this method has some shortcomings. This paper proposes a new Pareto optimal solution visualization method using an improved GHSOM based on the batch learning. In the proposed method, the batch learning algorithm is introduced to the GHSOM to obtain a consistent visualization maps for a Pareto optimal solution set. Then, the symmetric transformation of maps is introduced in the growing process in the batch learning GHSOM algorithm to improve readability of the maps. Furthermore, the learning parameter optimization is introduced. The effectiveness of the proposed method is confirmed through numerical experiments with comparing the proposed method to the conventional methods on the Pareto optimal solution representation capability and the readability of the visualization maps.

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 Exploring nucleon structure with the self-organizing maps algorithm

2015 ◽  
Vol 42 (3) ◽  
pp. 034030 ◽  
Author(s):  
Evan M Askanazi ◽  
Katherine A Holcomb ◽  
Simonetta Liuti
Keyword(s):  
The Self ◽  
Nucleon Structure ◽  
Self Organizing Maps ◽  
Self Organizing
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