There is a determinis- tic, fully-dynamic algorithm for the k-center clustering problem, where points are taken from a metric space with doubling dimension κ and aspect ratio ∆, such that the cost of the maintained solution is within a factor (2 + ε) to the cost of the optimal solution and the insertions and deletions ...
We present a deterministic dynamic algorithm for the k-center clustering problem that provably achieves a (2 + )-approximation in nearly logarithmic update and ...
Jan 7, 2021 · We study the metric k-center clustering problem in the fully dynamic setting, where the goal is to efficiently maintain a clustering while supporting an ...
The study aims at quantifying the topological characteristics and assessing the validity of static-robustness metrics as expressive measures of transit networks ...
Oct 15, 2024 · In this paper, we consider the metric k k k italic_k -center problem in the fully dynamic setting, where we are given a metric space ( V , d ) V ...
This work provides the first algorithm for solving the approximate furthest neighbor problem in metric spaces with finite doubling dimension and seems to be ...
We develop a (2+ε)-approximation algorithm for the k-center clustering problem with "small»» amortized cost under the fully dynamic adversarial model. In such a ...
We present a O(1)-approximate fully dynamic algorithm for the k-median and k- means problems on metric spaces with amortized update time˜O(k) and worst-case.
Jul 25, 2023 · The goal of clustering is to find a structure in data by grouping together similar data points. Specifically, for k-clustering objectives, the ...
In this paper, we consider the problem of dynamic k-center clustering with outliers. The (metric) k-center problem is to find a set C∗ ⊆ P of k points such ...