Is there benefit in multiple levels of clustering?
Think about generalizing your approach by searching for optimal numbers of clusters in both steps (combined) to minimize the number of clusters while maximizing your coverage. This is an objective where your method benefits.
I'm currently developing a clustering model and seeking something a bit novel.
I have looked at initial clustering to 5 clusters and then applying another run to cluster these into 2 each (so 10 total).
Results are similar but definitely not the same as if I ran just once for 10 clusters, rather than 5 and then 2.
Is there any obvious difference or benefit / drawback to such an approach? I cannot find much academia on this and potentially with good reason.