**4.2 U-matrix of the SOM, TKM and RSOM networks**

Table 2 shows a comparison among the U-Matrices of the networks studied. The U-Matrices are representations of the self-organizing networks where the Euclidean distance between the codebook vector of the neighbouring neurons is represented in a two-dimensional color scale image.

It is observed that the RSOM network presented the best view among the networks studied, distinguishing clearly the existence of three clusters in the data set used for training this neural network.

The ROC graph for the SOM, TKM and RSOM in 9x9 grid is presented in Figure 10. One notices that for this grid, the RSOM network has a larger tp rate and smaller fp rate for all three labels considered. This fact confirms even more the best performance observed for the

RSOM classifier, among all networks analyzed.

Fig. 10. ROC graph for the SOM, TKM and RSOM in 9x9 grid

Table 2 shows a comparison among the U-Matrices of the networks studied. The U-Matrices are representations of the self-organizing networks where the Euclidean distance between the codebook vector of the neighbouring neurons is represented in a two-dimensional color

It is observed that the RSOM network presented the best view among the networks studied, distinguishing clearly the existence of three clusters in the data set used for training this

**4.2 U-matrix of the SOM, TKM and RSOM networks** 

scale image.

neural network.

Legend: Color scale represent the Euclidean distance between the codebook vector of the neighbouring neurons

Table 2. U-matrix of the SOM, TKM and RSOM networks
