Online unit clustering in higher dimensions

@article{Dumitrescu2017OnlineUC,
  title={Online unit clustering in higher dimensions},
  author={Adrian Dumitrescu and Csaba D. T{\'o}th},
  journal={ArXiv},
  year={2017},
  volume={abs/1708.02662}
}
We revisit the online Unit Clustering problem in higher dimensions: Given a set of n points in \(\mathbb {R}^d\), that arrive one by one, partition the points into clusters (subsets) of diameter at most one, so as to minimize the number of clusters used. In this paper, we work in \(\mathbb {R}^d\) using the \(L_\infty \) norm. We show that the competitive ratio of any algorithm (deterministic or randomized) for this problem must depend on the dimension d. This resolves an open problem raised by… 
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References

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