I am trying to use clustering on certain data. The data itself has three natural levels: at the lowest level the elements are fundamental building blocks, at the second level these fundamental building blocks merge together to become larger element, and then at an even higher level they become largest elements. Analogically, they may be think of small particles, atoms and matter. This is just analogy.
I want to do hierarchical clustering for the lowest levels using the raw features. Then, when the threshold hits the point at the middle level (atoms), I want to start clustering them in terms of some aggregate features. And similarly, when I hit the third natural level (matter), I want to start clustering them using further aggregation of features.
Is there any specific variant of hierarchical clustering that is suitable for this purpose?
Edit: As it is pointed out in one of the comments, I am looking for an algorithm which performs hierarchical clustering while at certain levels can switch to different (aggregate of previous ones in some way) distance metrics.