Clustering with Neighborhoods

In the standard planar $k$-center clustering problem, one is given a set $P$ of $n$ points in the plane, and the goal is to select $k$ center points, so as to minimize the maximum distance over points in $P$ to their nearest center. Here we initiate the systematic study of the clustering with neighborhoods problem, which generalizes the $k$-center problem to allow the covered objects to be a set of general disjoint convex objects $\mathscr{C}$ rather than just a point set $P$. For this problem we first show that there is a PTAS for approximating the number of centers. Specifically, if $r{opt}$ is the optimal radius for $k$ centers, then in $n^{{O(1/\varepsilon2)}$} time we can produce a set of $(1+\varepsilon)k$ centers with radius $\leq r{opt}$. If instead one considers the standard goal of approximating the optimal clustering radius, while keeping $k$ as a hard constraint, we show that the radius cannot be approximated within any factor in polynomial time unless $\mathsf{P=NP}$, even when $\mathscr{C}$ is a set of line segments. When $\mathscr{C}$ is a set of unit disks we show the problem is hard to approximate within a factor of $\frac{\sqrt{13}-\sqrt{3}}{2-\sqrt{3}}\approx 6.99$. This hardness result complements our main result, where we show that when the objects are disks, of possibly differing radii, there is a $(5+2\sqrt{3})\approx 8.46$ approximation algorithm. Additionally, for unit disks we give an $O(n\log k)+(k/\varepsilon)^{O(k)}$ time $(1+\varepsilon)$-approximation to the optimal radius, that is, an FPTAS for constant $k$ whose running time depends only linearly on $n$. Finally, we show that the one dimensional version of the problem, even when intersections are allowed, can be solved exactly in $O(n\log n)$ time.