Ordering Process of Self-Organizing Maps Improved by Asymmetric Neighborhood Function

2008 ◽  
pp. 426-435
Author(s):  
Takaaki Aoki ◽  
Kaiichiro Ota ◽  
Koji Kurata ◽  
Toshio Aoyagi
Keyword(s):  
Self Organizing Maps ◽  
Neighborhood Function ◽  
Self Organizing

scholarly journals Ordering process of self-organizing maps improved by asymmetric neighborhood function

2008 ◽  
Vol 3 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Takaaki Aoki ◽  
Kaiichiro Ota ◽  
Koji Kurata ◽  
Toshio Aoyagi
Keyword(s):  
Self Organizing Maps ◽  
Neighborhood Function ◽  
Self Organizing

Self-Organizing Map Convergence

2018 ◽  
Vol 9 (2) ◽  
pp. 61-84 ◽  
Author(s):  
Robert Tatoian ◽  
Lutz Hamel
Keyword(s):  
Quality Measures ◽  
Training Data ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Data Set ◽  
Clustering Problem ◽  
Neighborhood Function ◽  
Real World Datasets ◽  
Clustering Problems ◽  
Self Organizing

Self-organizing maps are artificial neural networks designed for unsupervised machine learning. Here in this article, the authors introduce a new quality measure called the convergence index. The convergence index is a linear combination of map embedding accuracy and estimated topographic accuracy and since it reports a single statistically meaningful number it is perhaps more intuitive to use than other quality measures. The convergence index in the context of clustering problems was proposed by Ultsch as part of his fundamental clustering problem suite as well as real world datasets. First demonstrated is that the convergence index captures the notion that a SOM has learned the multivariate distribution of a training data set by looking at the convergence of the marginals. The convergence index is then used to study the convergence of SOMs with respect to the different parameters that govern self-organizing map learning. One result is that the constant neighborhood function produces better self-organizing map models than the popular Gaussian neighborhood function.

Programmable, Asynchronous, Triangular Neighborhood Function for Self-Organizing Maps Realized on Transistor Level

2010 ◽  
Vol 56 (4) ◽  
pp. 367-373
Author(s):  
Marta Kolasa ◽  
Rafał Długosz ◽  
Krzysztof Bieliński
Keyword(s):  
Circuit Complexity ◽  
Self Organizing Maps ◽  
Data Set ◽  
Ultra Low Power ◽  
Chip Area ◽  
Software Model ◽  
Neighborhood Function ◽  
The Neural Network ◽  
Data Rates ◽  
Self Organizing

Programmable, Asynchronous, Triangular Neighborhood Function for Self-Organizing Maps Realized on Transistor LevelA new hardware implementation of the triangular neighborhood function (TNF) for ultra-low power, Kohonen self-organizing maps (SOM) realized in the CMOS 0.18μm technology is presented. Simulations carried out by means of the software model of the SOM show that even low signal resolution at the output of the TNF block of 3-6 bits (depending on input data set) does not lead to significant disturbance of the learning process of the neural network. On the other hand, the signal resolution has a dominant influence on the overall circuit complexity i.e. the chip area and the energy consumption. The proposed neighborhood mechanism is very fast. For an example neighborhood range of 15 a delay between the first and the last neighboring neuron does not exceed 20 ns. This in practice means that the adaptation process starts in all neighboring neurons almost at the same time. As a result, data rates of 10-20 MHz are achievable, independently on the number of neurons in the map. The proposed SOM dissipates the power in-between 100 mW and 1 W, depending on the number of neurons in the map. For the comparison, the same network realized on PC achieves in simulations data rates in-between 10 Hz and 1 kHz. Data rate is in this case linearly dependend on the number of neurons.

Self-Organizing Maps with Asymmetric Neighborhood Function

2007 ◽  
Vol 19 (9) ◽  
pp. 2515-2535 ◽  
Author(s):  
Takaaki Aoki ◽  
Toshio Aoyagi
Keyword(s):  
Principal Component ◽  
Two Dimensions ◽  
Data Sets ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Neighborhood Function ◽  
Som Algorithm ◽  
The One ◽  
Low Dimensional ◽  
Self Organizing

The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space to a low-dimensional representation space. It has recently found wide applications in such areas as visualization, classification, and mining of various data. However, when the data sets to be processed are very large, a copious amount of time is often required to train the map, which seems to restrict the range of putative applications. One of the major culprits for this slow ordering time is that a kind of topological defect (e.g., a kink in one dimension or a twist in two dimensions) gets created in the map during training. Once such a defect appears in the map during training, the ordered map cannot be obtained until the defect is eliminated, for which the number of iterations required is typically several times larger than in the absence of the defect. In order to overcome this weakness, we propose that an asymmetric neighborhood function be used for the SOM algorithm. Compared with the commonly used symmetric neighborhood function, we found that an asymmetric neighborhood function accelerates the ordering process of the SOM algorithm, though this asymmetry tends to distort the generated ordered map. We demonstrate that the distortion of the map can be suppressed by improving the asymmetric neighborhood function SOM algorithm. The number of learning steps required for perfect ordering in the case of the one-dimensional SOM is numerically shown to be reduced from O(N3) to O(N2) with an asymmetric neighborhood function, even when the improved algorithm is used to get the final map without distortion.

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.

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