Instead of using ARI, you can try to evaluate the SOM by visualizing the results.
One common way to see how the data is being clustered by the SOM is by plotting the data points along with their corresponding neuron on a two-dimensional map.
Another approach is to use internal validation metrics, such as the quantization error or topographic error, which measure the distance between the input data and the nearest neuron on the SOM. These metrics can help you optimize the hyperparameters of the SOM, such as the grid size or learning rate.
Therefore, instead of relying solely on ARI or other traditional clustering metrics, you may want to use a combination of visualization, internal validation metrics, and comparison to other clustering methods to evaluate the performance of your SOM.
This guide on Iris Clustering using SOM might give you some insight on how to visualise the performance of your network:
You might also find these links useful:
