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Parameterized Approximation for Robust Clustering in Discrete Geometric Spaces. (arXiv:2305.07316v1 [cs.DS])
May 15, 2023, 12:43 a.m. | Fateme Abbasi, Sandip Banerjee, Jarosław Byrka, Parinya Chalermsook, Ameet Gadekar, Kamyar Khodamoradi, Dániel Marx, Roohani Sharma, Joachi
cs.LG updates on arXiv.org arxiv.org
We consider the well-studied Robust $(k, z)$-Clustering problem, which
generalizes the classic $k$-Median, $k$-Means, and $k$-Center problems. Given a
constant $z\ge 1$, the input to Robust $(k, z)$-Clustering is a set $P$ of $n$
weighted points in a metric space $(M,\delta)$ and a positive integer $k$.
Further, each point belongs to one (or more) of the $m$ many different groups
$S_1,S_2,\ldots,S_m$. Our goal is to find a set $X$ of $k$ centers such that
$\max_{i \in [m]} \sum_{p \in S_i} …
approximation arxiv center clustering delta positive set space spaces
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