Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Projective clustering in high dimensions using core-sets
Proceedings of the eighteenth annual symposium on Computational geometry
DIALM '02 Proceedings of the 6th international workshop on Discrete algorithms and methods for mobile computing and communications
Smooth kinetic maintenance of clusters
Proceedings of the nineteenth annual symposium on Computational geometry
A (1 + ɛ)-approximation algorithm for 2-line-center
Computational Geometry: Theory and Applications
The Journal of Machine Learning Research
Mobile Networks and Applications - Discrete algorithms and methods for mobile computing and communications
On coresets for k-means and k-median clustering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Smooth kinetic maintenance of clusters
Computational Geometry: Theory and Applications - Special issue on the 19th annual symposium on computational geometry - SoCG 2003
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Bi-criteria linear-time approximations for generalized k-mean/median/center
SCG '07 Proceedings of the twenty-third annual symposium on Computational geometry
The Kinetic Facility Location Problem
SWAT '08 Proceedings of the 11th Scandinavian workshop on Algorithm Theory
Proceedings of the forty-first annual ACM symposium on Theory of computing
Smooth kinetic maintenance of clusters
Computational Geometry: Theory and Applications - Special issue on the 19th annual symposium on computational geometry - SoCG 2003
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Net and prune: a linear time algorithm for euclidean distance problems
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
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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 (n\log K) time (which is optimal in the comparison model).