<p>a). Classification of monitoring sites based on their similarities from physicochemical variables on SOM output layer. b). Hierarchical clustering according to the similarity between SOM neurons. c). Map of the clustering sites in the LMB. The acronyms in the hexagonal neurons represent the monitoring sites. The sample code is composed of 5 characters; the first character is a number from 1 to 4 indicating the country code: 1 for Cambodia, 2 for Laos, 3 for Thailand and 4 for Vietnam. The rest indicates the water body type and number of sites along this water body (i.e. MK-Mekong river, BS: Bassac river, MT: Mekong tributary, VD: Vietnam delta, DC: Delta canal, TS: Tonle Sap lake, WL: Swamp).</p
Self-Organizing Maps (SOMs) have been useful in gaining insights about the information content of la...
This release allows performing a combined SOM/SuperSOM clustering of the 640 administrative district...
The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algo...
<p>The left side represents the distribution in the 64 neurons of lncRNAs. Every digit of the hexago...
The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimen-s...
<p>(a) The population map shows the localization of all 134 body measures in SOM space. The color co...
(A). SOM trained to analyse the respective bacteria illustrating the clustering of the spectra. (B)....
This work presents a neural network model for the clustering analysis of data based on Self Organizi...
The utilization of mathematical and computational tools for pollutant assessment frameworks has beco...
Numbered clusters correspond to the most common environments: Continental shelf (Cluster 1), Contine...
<p>A SOM analysis was performed to group spots with a similar expression pattern, in this way cluste...
Self-organizing map (SOM) [1] is an artificial intelligence method for clustering, visualization and...
The left panel shows the SOM map of codes indicating the underlying distribution of variables involv...
Three classification techniques (loading and score projections based on principal components analysi...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
Self-Organizing Maps (SOMs) have been useful in gaining insights about the information content of la...
This release allows performing a combined SOM/SuperSOM clustering of the 640 administrative district...
The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algo...
<p>The left side represents the distribution in the 64 neurons of lncRNAs. Every digit of the hexago...
The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimen-s...
<p>(a) The population map shows the localization of all 134 body measures in SOM space. The color co...
(A). SOM trained to analyse the respective bacteria illustrating the clustering of the spectra. (B)....
This work presents a neural network model for the clustering analysis of data based on Self Organizi...
The utilization of mathematical and computational tools for pollutant assessment frameworks has beco...
Numbered clusters correspond to the most common environments: Continental shelf (Cluster 1), Contine...
<p>A SOM analysis was performed to group spots with a similar expression pattern, in this way cluste...
Self-organizing map (SOM) [1] is an artificial intelligence method for clustering, visualization and...
The left panel shows the SOM map of codes indicating the underlying distribution of variables involv...
Three classification techniques (loading and score projections based on principal components analysi...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
Self-Organizing Maps (SOMs) have been useful in gaining insights about the information content of la...
This release allows performing a combined SOM/SuperSOM clustering of the 640 administrative district...
The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algo...