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In the classical k-means problem, we are given a data set and an integer k. The object is to select a set of size at most k such that each point in is connected to the closet cluster in S with minimum total squared distances. However, in some real-life applications, it is more desirable and beneficial to pay a small penalty for not connecting some outliers in that are too far away from most points. As a result, we are motivated to study the k-means problem with penalties, for which we propose a -approximation algorithm via the primal-dual technique, improving the previous best approximation ratio of in[7] also by using the primal-dual technique. © 2020, Springer Nature Switzerland AG.
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ISSN: 0302-9743
年份: 2020
卷: 12337 LNCS
页码: 377-389
语种: 英文
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