@article {15257,
	title = {Streaming algorithms for k-center clustering with outliers and with anonymity},
	journal = {Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques},
	year = {2008},
	month = {2008///},
	pages = {165 - 178},
	abstract = {Clustering is a common problem in the analysis of large data sets. Streaming algorithms, which make a single pass over the data set using small working memory and produce a clustering comparable in cost to the optimal offline solution, are especially useful. We develop the first streaming algorithms achieving a constant-factor approximation to the cluster radius for two variations of the k-center clustering problem. We give a streaming (4 + ε)-approximation algorithm using O(ε - 1 kz) memory for the problem with outliers, in which the clustering is allowed to drop up to z of the input points; previous work used a random sampling approach which yields only a bicriteria approximation. We also give a streaming (6 + ε)-approximation algorithm using O(ε - 1 ln (ε - 1) k + k 2) memory for a variation motivated by anonymity considerations in which each cluster must contain at least a certain number of input points.},
	doi = {10.1007/978-3-540-85363-3_14},
	author = {Matthew McCutchen,R. and Khuller, Samir}
}
