@inproceedings{47ef0c3ee2b742f0b91d93ed9cf729b1,

title = "Smallest enclosing ball for probabilistic data",

abstract = "This paper deals with computing the smallest enclosing ball of a set of points subject to probabilistic data. In our setting, any of the n points may not or may occur at one of finitely many locations, following its own discrete probability distribution. The objective is therefore considered to be a random variable and we aim at finding a center minimizing the expected maximum distance to the points according to their distributions. Our main contribution presented in this paper is the first polynomial time (1 + ε)-approximation algorithm for the probabilistic smallest enclosing ball problem with extensions to the streaming setting.",

keywords = "1-median, Probabilistic data, Sampling, Smallest enclosing ball",

author = "Alexander Munteanu and Christian Sohler and Dan Feldman",

year = "2014",

doi = "10.1145/2582112.2582114",

language = "English",

isbn = "9781450325943",

series = "Proceedings of the Annual Symposium on Computational Geometry",

publisher = "Association for Computing Machinery",

pages = "214--223",

booktitle = "Proceedings of the 30th Annual Symposium on Computational Geometry, SoCG 2014",

note = "30th Annual Symposium on Computational Geometry, SoCG 2014 ; Conference date: 08-06-2014 Through 11-06-2014",

}