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

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Keywords : algorithm - solution - sigma - kz - space -

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