Tight FPT Approximations for k-Median and k-Means

04/28/2019
by   Vincent Cohen-Addad, et al.
0

We investigate the fine-grained complexity of approximating the classical k-median / k-means clustering problems in general metric spaces. We show how to improve the approximation factors to (1+2/e+ε) and (1+8/e+ε) respectively, using algorithms that run in fixed-parameter time. Moreover, we show that we cannot do better in FPT time, modulo recent complexity-theoretic conjectures.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/30/2018

Coresets for k-Means and k-Median Clustering and their Applications

In this paper, we show the existence of small coresets for the problem...
research
08/30/2022

On the Fixed-Parameter Tractability of Capacitated Clustering

We study the complexity of the classic capacitated k-median and k-means ...
research
12/20/2018

Near-Linear Time Approximation Schemes for Clustering in Doubling Metrics

We consider the classic Facility Location, k-Median, and k-Means problem...
research
02/22/2023

The Power of Uniform Sampling for k-Median

We study the power of uniform sampling for k-Median in various metric sp...
research
06/03/2013

Distributed k-Means and k-Median Clustering on General Topologies

This paper provides new algorithms for distributed clustering for two po...
research
06/23/2022

The quarter median

We introduce and discuss a multivariate version of the classical median ...
research
04/11/2022

Improved Approximations for Euclidean k-means and k-median, via Nested Quasi-Independent Sets

Motivated by data analysis and machine learning applications, we conside...

Please sign up or login with your details

Forgot password? Click here to reset