Lossy Kernelization of Same-Size Clustering
Data Structures and Algorithms
2021-07-16 v1
Abstract
In this work, we study the -median clustering problem with an additional equal-size constraint on the clusters, from the perspective of parameterized preprocessing. Our main result is the first lossy (-approximate) polynomial kernel for this problem, parameterized by the cost of clustering. We complement this result by establishing lower bounds for the problem that eliminate the existences of an (exact) kernel of polynomial size and a PTAS.
Keywords
Cite
@article{arxiv.2107.07383,
title = {Lossy Kernelization of Same-Size Clustering},
author = {Sayan Bandyapadhyay and Fedor V. Fomin and Petr A. Golovach and Nidhi Purohit and Kirill Simonov},
journal= {arXiv preprint arXiv:2107.07383},
year = {2021}
}