An Empirical Evaluation of $k$-Means Coresets
Abstract
Coresets are among the most popular paradigms for summarizing data. In particular, there exist many high performance coresets for clustering problems such as -means in both theory and practice. Curiously, there exists no work on comparing the quality of available -means coresets. In this paper we perform such an evaluation. There currently is no algorithm known to measure the distortion of a candidate coreset. We provide some evidence as to why this might be computationally difficult. To complement this, we propose a benchmark for which we argue that computing coresets is challenging and which also allows us an easy (heuristic) evaluation of coresets. Using this benchmark and real-world data sets, we conduct an exhaustive evaluation of the most commonly used coreset algorithms from theory and practice.
Cite
@article{arxiv.2207.00966,
title = {An Empirical Evaluation of $k$-Means Coresets},
author = {Chris Schwiegelshohn and Omar Ali Sheikh-Omar},
journal= {arXiv preprint arXiv:2207.00966},
year = {2022}
}