An Improved Algorithm for Online Unit Clustering

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.

• Type: Conference
• Year: 2007
• Where: COCOON
• Authors: Hamid Zarrabi-Zadeh, Timothy M. Chan
• Keywords: COCOON 2007, Combinatorics, Randomized Online Algorithm, Unit Clustering Problem, Unit Interval