I am using SOM for dimension reduction and visualization purpose (to put the same observations together). I am using
kohonen r-package for the same.
For experimental purpose I took a 2-dimensional data (original):
- Applied hierarchical clustering on original data.
- Applied SOM on original data and then applied hierarchical clustering on top of that.
I got similar results.
So my question is: Is preserving the topological distances only advantage behind using SOM over clustering?
If yes, then why this is important? If no, what are other advantages of SOM over clustering?