SCG '94 Proceedings of the tenth annual symposium on Computational geometry
Fixed-Parameter Tractability and Completeness I: Basic Results
SIAM Journal on Computing
Approximation schemes for Euclidean k-medians and related problems
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems
Journal of the ACM (JACM)
A constant-factor approximation algorithm for the k-median problem (extended abstract)
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
A local search approximation algorithm for k-means clustering
Proceedings of the eighteenth annual symposium on Computational geometry
Lectures on Discrete Geometry
A Nearly Linear-Time Approximation Scheme for the Euclidean kappa-median Problem
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
Approximation schemes for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
On coresets for k-means and k-median clustering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Optimal time bounds for approximate clustering
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Coresets in dynamic geometric data streams
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
On k-Median clustering in high dimensions
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
A fast k-means implementation using coresets
Proceedings of the twenty-second annual symposium on Computational geometry
How to get close to the median shape
Proceedings of the twenty-second annual symposium on Computational geometry
How to get close to the median shape
Computational Geometry: Theory and Applications - Special issue on the 21st European workshop on computational geometry (EWCG 2005)
A PTAS for k-means clustering based on weak coresets
SCG '07 Proceedings of the twenty-third annual symposium on Computational geometry
Summarizing spatial data streams using ClusterHulls
Journal of Experimental Algorithmics (JEA)
Facility Location in Dynamic Geometric Data Streams
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
Coresets and approximate clustering for Bregman divergences
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Single facility collection depots location problem in the plane
Computational Geometry: Theory and Applications
Proceedings of the forty-first annual ACM symposium on Theory of computing
Small space representations for metric min-sum k-clustering and their applications
STACS'07 Proceedings of the 24th annual conference on Theoretical aspects of computer science
Universal ε-approximators for integrals
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Improving the performance of k-means for color quantization
Image and Vision Computing
Coresets for discrete integration and clustering
FSTTCS'06 Proceedings of the 26th international conference on Foundations of Software Technology and Theoretical Computer Science
Algorithmic superactivation of asymptotic quantum capacity of zero-capacity quantum channels
Information Sciences: an International Journal
Net and prune: a linear time algorithm for euclidean distance problems
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Hi-index | 0.00 |
In this paper, we show that there exists a (k, ε)-coreset for k-median and k-means clustering of n points in Rd, which is of size independent of n. In particular, we construct a (k, ε)-coreset of size O(k2/εd) for k-median clustering, and of size O(k3/εd+1) for k-means clustering.