Suppose we have a given dataset of $M$ points and in an $N$-dimensional space. We train a 2D self-organising map (SOM) with this dataset. Then, if the SOM has dimensions $d_1\times d_2$, then there will be $P=d_1d_2$ points of dimension $N$ that represent the original dataset. In general, $P<<M$.
Would it make sense to cluster the $P$ neurons of the SOM instead of the $M$ original datapoints? The procedure I imagine would be as follows:
Does this approach make sense? In which cases would it be sensible to follow this procedure? Is this done in real life?