A Broader View on Clustering under Cluster-Aware Norm Objectives

• URL: http://arxiv.org/abs/2512.06211v2
• Date: Tue, 09 Dec 2025 10:18:53 GMT
• Title: A Broader View on Clustering under Cluster-Aware Norm Objectives
• Authors: Martin G. Herold, Evangelos Kipouridis, Joachim Spoerhase,
• Abstract summary: We revisit the $(f,g)$-clustering problem that we introduced in a recent work.<n>This problem assigns each of the $k$ clusters a cost determined by the monotone, symmetric norm $f$ applied to the vector distances in the cluster.<n>We provide an $O(k)$-approximation for the general $(f,g)$-clustering problem.
• Score: 0.8016305126057338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We revisit the $(f,g)$-clustering problem that we introduced in a recent work [SODA'25], and which subsumes fundamental clustering problems such as $k$-Center, $k$-Median, Min-Sum of Radii, and Min-Load $k$-Clustering. This problem assigns each of the $k$ clusters a cost determined by the monotone, symmetric norm $f$ applied to the vector distances in the cluster, and aims at minimizing the norm $g$ applied to the vector of cluster costs. Previously, we focused on certain special cases for which we designed constant-factor approximation algorithms. Our bounds for more general settings left, however, large gaps to the known bounds for the basic problems they capture. In this work, we provide a clearer picture of the approximability of these more general settings. First, we design an $O(\log^2 n)$-approximation algorithm for $(f, L_{1})$-clustering for any $f$. This improves upon our previous $\widetilde{O}(\sqrt{n})$-approximation. Second, we provide an $O(k)$-approximation for the general $(f,g)$-clustering problem, which improves upon our previous $\widetilde{O}(\sqrt{kn})$-approximation algorithm and matches the best-known upper bound for Min-Load $k$-Clustering. We then design an approximation algorithm for $(f,g)$-clustering that interpolates, up to polylog factors, between the best known bounds for $k$-Center, $k$-Median, Min-Sum of Radii, Min-Load $k$-Clustering, (Top, $L_{1}$)-clustering, and $(L_{\infty},g)$-clustering based on a newly defined parameter of $f$ and $g$.

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