I have a series of 2D coordinates X = {x, y}. Each are associated with one categorical variable W that can take 7 different values.
E.g:
coord W
X1 3
X2 5
X3 7
X4 3
X5 2
X6 3
X7 2
...
X2000 5
...
I would like to get all the clusters that belong to a given set of my categorical variable within 2 pixels of each other. Say, for the triplet of W values (2, 5, 7), or a tuplet of all values (1,2,3,4,5,6,7), I want to have all the sets (if any) of coordinates that are within 2 pixels of each other. What would be the most appropriate methods?
Also, there are no singletons, all these coordinates have at least another nearest neighbour with a different W value. I only know how to do this to only find the sets of pairs of coordinates for 2 different W values (using pair-wise euclidian distance matrix), but to get clusters of more elements for more than 2 of my W values, I am confused about what clustering method to use (and if that actually falls in the realm of clustering at all...), as that seems rather basic and I keep reading about rather sophisticated approaches that look overkill (KNN, HDBSCAN, ...).