For points forming a 2D Pareto front, discrete and continuous K-center clustering are solvable in near-linear O(KN polylog N) time and O(N) memory via dynamic programming over sorted intervals.
Computing k-Centers On a Line
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abstract
In this paper we consider several instances of the k-center on a line problem where the goal is, given a set of points S in the plane and a parameter k >= 1, to find k disks with centers on a line l such that their union covers S and the maximum radius of the disks is minimized. This problem is a constraint version of the well-known k-center problem in which the centers are constrained to lie in a particular region such as a segment, a line, and a polygon. We first consider the simplest version of the problem where the line l is given in advance; we can solve this problem in O(n log^2 n) time. We then investigate the cases where only the orientation of the line l is fixed and where the line l can be arbitrary. We can solve these problems in O(n^2 log^2 n) time and in O(n^4 log^2 n) expected time, respectively. For the last two problems, we present (1 + e)-approximation algorithms, which run in O((1/e) n log^2 n) time and O((1/e^2) n log^2 n) time, respectively.
fields
cs.CG 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Planar p-center problems are solvable in polynomial time when clustering a Pareto Front
For points forming a 2D Pareto front, discrete and continuous K-center clustering are solvable in near-linear O(KN polylog N) time and O(N) memory via dynamic programming over sorted intervals.