Better Bounds on Online Unit Clustering

Martin R. Ehmsen, Kim Skak Larsen

Research output: Contribution to journalJournal articleResearchpeer-review


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 the work, recently 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.
Original languageEnglish
JournalTheoretical Computer Science
Pages (from-to)1-24
Number of pages24
Publication statusPublished - 2013


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