A Comparative Study of the Use of Coresets for Clustering Large Datasets

Abstract

Coresets can be described as a compact subset such that models trained on coresets will also provide a good fit with models trained on full data set. By using coresets, we can scale down a big data to a tiny one in order to reduce the computational cost of a machine learning problem. In recent years, data scientists have investigated various methods to create coresets. The two state-of-the-art algorithms have been proposed in 2018 are ProTraS by Ros & Guillaume and Lightweight Coreset by Bachem et al. In this paper, we briefly introduce these two algorithms and make a comparison between them to find out the benefits and drawbacks of each one.