An improved data stream algorithm for clustering

摘要

In the k-center problem for streaming points in d-dimensional metric space, input points are given in a data stream and the goal is to find the k smallest congruent balls whose union covers all input points by examining them. In the single-pass streaming model, input points are allowed to be examined only once and the amount of space that can be used to store relative information is limited.