SOM automated/objective clustering So as I understand it SOM is primarily a visualization tool and clustering is a logical next step after you construct a SOM from data. Typically, the clustering is subjective in that after looking at your SOM you can 'see' N clusters and then from there you would go on to cluster (k means, hierarchical etc.) the SOM nodes with this N as your parameter. Is there a nonsubjective (maybe nonparametric?) way to cluster the SOM nodes (i.e. without first looking at the SOM to determine the number of distinct clusters)? Thanks
 A: To answer your question, yes there are. There are numerical ways to determine the best cluster form. However, it is subjective which one you use.
I do not think you are still looking for this 5 years after date but this can hopefully help people in the future.
If you have any question on clustering, see @Ben 's elaborate answer in how to determine optimal cluster
Here he discusses 8 ways to determine the number of clusters:


*

*look for a bend in the WSS elbow plot (I am currently using this, because it's easy, however, difficult to automate)

*Partitioning around mediods with the pamk function of fmc in R

*Calinsky criterion (I am not a fan, don't understand the background that well)

*Determine the optimal model and number of clusters according to the Bayesian Information Criterion for expectation-maximization. This is nice, maybe the closest you will get to feeling it is not subjective, because it gives an 'optimum'. 

*Affinity propagation (AP) clustering

*Gap Statistic for Estimating the Number of Clusters

*clustergrams to visualize cluster assignment, see http://www.r-statistics.com/2010/06/clustergram-visualization-and-diagnostics-for-cluster-analysis-r-code/ 

*The NbClust package
Ben explains each one with examples with nice plots. 
A: If you don't know a priori the number of clusters, I don't think SOM will be of much help. However, you can take a look at some SOM variants, like  LARFSOM, which is a SOM that inserts/removes nodes on the fly - and it was conceived by my graduate advisor :) The number of nodes in the end is expected to be the correct number of clusters in the dataset.
