Affiliations: [a] The Czech Academy of Sciences, Institute of Computer Science, Prague, Czech Republic. keikha@cs.cas.cz | [b] Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. aghamolaei@ce.sharif.edu | [c] Department of Mathematics and Computer Sci., Amirkabir University of Technology, Tehran, Iran. mohades@aut.ac.ir | [d] Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. ghodsi@sharif.edu
Correspondence:
[*]
Address for correspondence: The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 182 07 Prague, Czech Republic.
Note: [†] Also affiliated at: Department of Computer Science, University of Sistan and Baluchestan, Zahedan, Iran.
Note: [‡] Also affiliated at: School of CS, IPM, Tehran, Iran.
Abstract: The k-center problem is to choose a subset of size k from a set of n points such that the maximum distance from each point to its nearest center is minimized. Let Q = {Q1, . . . , Qn} be a set of polygons or segments in the region-based uncertainty model, in which each Qi is an uncertain point, where the exact locations of the points in Qi are unknown. The geometric objects such as segments and polygons can be models of a point set. We define the uncertain version of the k-center problem as a generalization in which the objective is to find k points from Q to cover the remaining regions of Q with minimum or maximum radius of the cluster to cover at least one or all exact instances of each Qi, respectively. We modify the region-based model to allow multiple points to be chosen from a region, and call the resulting model the aggregated uncertainty model. All these problems contain the point version as a special case, so they are all NP-hard with a lower bound 1.822 for the approximation factor. We give approximation algorithms for uncertain k-center of a set of segments and polygons. We also have implemented some of our algorithms on a data-set to show our theoretical performance guarantees can be achieved in practice.
Keywords: k-center Uncertain data Approximation algorithms
