We present the first algorithm for the $k$-center problem with outliers in the sliding-window model . The algorithm is deterministic and uses $O((kz/\varepsilon^d) storage, where$d$is the doubling dimension of the underlying space and$sigma$is spread of the points in the stream . We present a lower bound showing that any algorithm that provides a$(1+\varapsilon)$-approximation must use$\Omega(kz)$storage. The algorithm works for the case where the pointscome from a space of bounded doubling dimension and it maintains a set$S(t)$such that an optimal solution on$P(t), such that the optimal solution gives a $(1+ \sigma)’solution on$S(‘P)$is$P(‘P(‘S(‘S)”)”)””))”)””S)” is $(1)””P(“P”)”)”P””P”) is$1″ is $2″ and$1(T)” is a

Author(s) : Mark de Berg, Morteza Monemizadeh, Yu Zhong