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- W2912011375 abstract "Given a set of moving points in mathbb{R}^d , we show that one can cluster them in advance, using a small number of clusters, so that in any point in time this static clustering is competitive with the optimal k-center clustering of the point-set at this point in time. The advantage of this approach is that it avoids the usage of kinetic data-structures and as such itdoes not need to update the clustering as time passes.To implement this static clustering efficiently, we describe a simple technique for speeding-up clustering algorithms, and apply it to achieve a faster clustering algorithms for several problems. In particular, we present a linear time algorithm for computing a 2-approximation to the k-center clustering of a set of n points in mathbb{R}^d . This slightlyimproves over the algorithm of Feder and Greene [9], that runs in Theta (nlog K) time (which is optimal in the comparison model)." @default.
- W2912011375 created "2019-02-21" @default.
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- W2912011375 date "2001-10-14" @default.
- W2912011375 modified "2023-09-23" @default.
- W2912011375 title "Clustering Motion" @default.
- W2912011375 hasPublicationYear "2001" @default.
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