An improved algorithm for online unit clustering

  • Authors:
  • Hamid Zarrabi-Zadeh;Timothy M. Chan

  • Affiliations:
  • School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada;School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada

  • Venue:
  • COCOON'07 Proceedings of the 13th annual international conference on Computing and Combinatorics
  • Year:
  • 2007

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Abstract

We revisit the online unit clustering problem in one dimension which we recently introduced at WAOA'06: given a sequence of n points on the line, the objective is to partition the points into a minimum number of subsets, each enclosable by a unit interval. We present a new randomized online algorithm that achieves expected competitive ratio 11/6 against oblivious adversaries, improving the previous ratio of 15/8. This immediately leads to improved upper bounds for the problem in two and higher dimensions as well.