I'm rather new to this field and maybe even using a bit naive terminology.
So I need to classify (cluster) some objects, but limiting each cluster in size, so for each cluster $C_i$,
$$\sum_{o \in C_i} m(o) \leq N$$
where $m(\cdot)$ is a some norm-like function.
In case the objects are on a euclidean plane, would be nice if also $\forall i \neq j, h(C_i) \cap j(C_j) = \emptyset $, where $h(\cdot)$ is a convex hull (though not strictly required)
Lots of thanks in advance for correcting my terminology, and links to both algorithm implementations and theory.
P.S. Without this limitation, we are more or less happy with DBSCAN results.
// Best regards, Sergei