Self-organizing map: How to identify clusters from plots?

Question

I've been learning about neural networks and most recently been trying out different clustering methods. But unlike KNN, GMM, or DBSCAN, there isn't a feature (in Matlab that I'm aware of) that identifies clusters for you. So I've been reading articles of how to interpret these plots, but I'm still confused. For my example, in the weight positions plot, I see one cluster. For the neighbor weight differences, I see one, maybe two clusters (yellow/bright - similar, red/dark - dissimilar). That seems to be confirmed when looking at the densities in the hits plot. There might be more, but I honestly I can't tell (I'm new at this) because of the gradient instead of a solid boundary between clusters. How many clusters do you see, and what's your logic? Thank you]1[]2[]3

selforgmap([5 5] [net,tr] = train(net,x) figure, plotsomnd(net) figure, plotsomhits(net,x) figure, plotsompos(net,x)

Answer 1

You may construct a new paradigm in relation with what the SOM nodes represent, i.e. they produce a new dataset. The new dataset is independent from the original dateset. Nevertheles, it is arranged somehow so that the underlying structure imitates that of the original dataset. Therefore, it is often found that people perform SOM with clustering algorithms such as K-means, Hierarchical Clustering, etc subsequently. This can be regarded as: instead of clustering directly from a huge amount of the original data, the clustering procedure is performed on a new version of the original dataset which is smaller but still inherits the topology of the original dataset. AFAIK, SOM is different from KNN in the sense that SOM is unsupervised whereas KNN is supervised.