Self-organizing maps (SOMs) are popular tools for grouping and visualizing data in many
areas of science. This paper describes recent changes in package kohonen, implementing …

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 …

The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network
algorithms, with thousands of applications covered in the literature. It was one of the strong …

The self-organizing map (SOM) network was originally designed for solving problems that
involve tasks such as clustering, visualization, and abstraction. While Kohonen's SOM …

Kohonen Network - Scholarpedia Kohonen Network From Scholarpedia Teuvo Kohonen
and Timo Honkela (2007), Scholarpedia, 2(1):1568. doi:10.4249/scholarpedia.1568 …

We present two enhanced visualization techniques for the self-organizing map allowing the
intuitive representation of input data similarity. The general idea of both approaches is to …

An overview on the usage of emergent self organizing maps is given. U-Maps visualize the
distance structures of high dimensional data sets. P-Maps show their density structures and …

We show that the number of output units used in a self-organizing map (SOM) influences its
applicability for either clustering or visualization. By reviewing the appropriate literature and …

Several methods to visualize clusters in high-dimensional data sets using the Self-
Organizing Map (SOM) have been proposed. However, most of these methods only focus on …

The Self-Organizing Map (SOM) represents the result of a vector quantization algorithm that
places a number of reference or codebook vectors into a high-dimensional input data space …