sorry for lame question, but I just have no maturity in this direction.
Let's say I have very large set ( millions ) of high-dimensional vectors ( typical dimensionality is 64). These vectors typically come from embedding of social network into a vector space, but this is not so important. Here is what we see using tSNE, for example:
We know, that sometimes some subsets of these points could form almost regular lattices like that: I need either: 1. To prove, that no such structures exist 2. Or find all these structures ( if any )
But the hard part of a problem is that these regular structures could be different, and no one can say -- what type of lattice could approximate these better than another?
It seems, that I have to find some clusters in my vector space, which are not just "dense", but also have some unknown form of regularity. And typical algorithms like DBSCAN or MeanShift will not help me much.
So, could you please give me a couple of hints : what articles to read on this subject ( at least to have more clear problem statement )?