U-matrix (unified distance matrix) representation of the Self-Organizing Map  visualizes the distances between the neurons. The distance between the adjacent neuons is calculated and presented with different colorings between the adjacent nodes. A dark coloring between the neurons corresponds to a large distance and thus a gap between the codebook values in the input space. A light coloring between the neurons signifies that the codebook vectors are close to each other in the input space. Light areas can be thought as clusters and dark areas as cluster separators. This can be a helpful presentation when one tries to find clusters in the input data without having any a priori information about the clusters.
Figure 2.8: U-matrix representation of the Self-Organizing Map
In the Figure 2.8 we can see the neurons of the network marked as black dots. The representation reveals that these are a separate cluster in the upper right corner of this representation. The clusters are separated by a dark gap. This result was achieved by unsupervised learning, that is, without human intervention. Teaching a SOM and representing it with the U-matrix offers a fast way to get insight of the data distribution.