# Approximation Algorithms for Clustering with Dynamic Points

arXiv: https://arxiv.org/abs/2006.14403 · HTML: https://mlsanity.com/paper/2006.14403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Dynamic Algorithm for Explainable k-medians Clustering under lp Norm](https://mlsanity.com/paper/2512.01150/) (2025)

## Abstract (arXiv)

We study two generalizations of classic clustering problems called dynamic ordered k-median and dynamic k-supplier, where the points that need clustering evolve over time, and we are allowed to move the cluster centers between consecutive time steps. In these dynamic clustering problems, the general goal is to minimize certain combinations of the service cost of points and the movement cost of centers, or to minimize one subject to some constraints on the other. We obtain a constant-factor approximation algorithm for dynamic ordered k-median under mild assumptions on the input. We give a 3-approximation for dynamic k-supplier and a multi-criteria approximation for its outlier version where some points can be discarded, when the number of time steps is two. We complement the algorithms with almost matching hardness results.