In this age of ever-increasing data set sizes, especially in the natural sciences, visualisation becomes more and more important. Self-organizing maps have many features that make them attractive in this respect: they do not rely on distributional assumptions, can handle huge data sets with ease, and have shown their worth in a large number of applications. In this paper, we highlight the kohonen package for R, which implements self-organizing maps as well as some extensions for supervised pattern recognition and data fusion
Artificial Intelligence Lab, Department of MIS, University of ArizonaThe Kohonen Self-Organizing Map...
International audienceThe Self-Organizing Map (SOM) is widely used, easy to implement , has nice pro...
Self-organizing map has been applied to a variety of tasks including data visualization and clusteri...
In this age of ever-increasing data set sizes, especially in the natural sciences, visualisation bec...
Self-organizing maps (SOMs) are popular tools for grouping and visualizing data in many areas of sci...
Self-organizing maps (SOMs) are popular tools for grouping and visualizing data in many areas of sci...
We will show that the number of output units used in a self-organizing map (SOM) influences its appl...
The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning techn...
Kohonen Self Organizing Maps (SOM) has found application in practical all fields, especially those w...
Currently, there exist many research areas that produce large multivariable datasets that are diffic...
The Self-Organizing Maps (SOM) is a very popular algorithm, introduced by Teuvo Kohonen in the early...
Over the years, the self-organizing map (SOM) algorithm was proven to be a powerful and convenient t...
Cluster analysis is the name given to a diverse collection of techniques that can be used to classif...
A review of recent development of the self-organising map (SOM) for applications related to data map...
Special Issue of the Neural Networks Journal after WSOM 05 in ParisNeural Networks Special Issue WSO...
Artificial Intelligence Lab, Department of MIS, University of ArizonaThe Kohonen Self-Organizing Map...
International audienceThe Self-Organizing Map (SOM) is widely used, easy to implement , has nice pro...
Self-organizing map has been applied to a variety of tasks including data visualization and clusteri...
In this age of ever-increasing data set sizes, especially in the natural sciences, visualisation bec...
Self-organizing maps (SOMs) are popular tools for grouping and visualizing data in many areas of sci...
Self-organizing maps (SOMs) are popular tools for grouping and visualizing data in many areas of sci...
We will show that the number of output units used in a self-organizing map (SOM) influences its appl...
The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning techn...
Kohonen Self Organizing Maps (SOM) has found application in practical all fields, especially those w...
Currently, there exist many research areas that produce large multivariable datasets that are diffic...
The Self-Organizing Maps (SOM) is a very popular algorithm, introduced by Teuvo Kohonen in the early...
Over the years, the self-organizing map (SOM) algorithm was proven to be a powerful and convenient t...
Cluster analysis is the name given to a diverse collection of techniques that can be used to classif...
A review of recent development of the self-organising map (SOM) for applications related to data map...
Special Issue of the Neural Networks Journal after WSOM 05 in ParisNeural Networks Special Issue WSO...
Artificial Intelligence Lab, Department of MIS, University of ArizonaThe Kohonen Self-Organizing Map...
International audienceThe Self-Organizing Map (SOM) is widely used, easy to implement , has nice pro...
Self-organizing map has been applied to a variety of tasks including data visualization and clusteri...