Better bounds on online unit clustering

Authors:
Martin R. Ehmsen;Kim S. Larsen
Affiliations:
Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark;Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
Venue:
SWAT'10 Proceedings of the 12th Scandinavian conference on Algorithm Theory
Year:
2010

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Abstract

Unit Clustering is the problem of dividing a set of points from a metric space into a minimal number of subsets such that the points in each subset are enclosable by a unit ball. We continue work initiated by Chan and Zarrabi-Zadeh on determining the competitive ratio of the online version of this problem. For the one-dimensional case, we develop a deterministic algorithm, improving the best known upper bound of 7/4 by Epstein and van Stee to 5/3. This narrows the gap to the best known lower bound of 8/5 to only 1/15. Our algorithm automatically leads to improvements in all higher dimensions as well. Finally, we strengthen the deterministic lower bound in two dimensions and higher from 2 to 13/6.