@article{bc0b727467594e9489dbf6de3890bc2d,
title = "NP-Hardness of balanced minimum sum-of-squares clustering",
abstract = "The balanced clustering problem consists of partitioning a set of n objects into K equal-sized clusters as long as n is a multiple of K. A popular clustering criterion when the objects are points of a q-dimensional space is the minimum sum of squared distances from each point to the centroid of the cluster to which it belongs. We show in this paper that this problem is NP-hard in general dimension already for triplets, i.e., when n/K=3.",
keywords = "Balanced clustering, Complexity, Sum-of-squares, COMPLEXITY",
author = "Artem Pyatkin and Daniel Aloise and Nenad Mladenovi{\'c}",
note = "Funding Information: This research was partially supported by RFBR, projects 16-07-00168 and 15-01-00462, by RSF grant 14-41-00039, and by CNPq/Brazil grants 308887/2014-0 and 400350/2014-9. Publisher Copyright: {\textcopyright} 2017 Elsevier B.V. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.",
year = "2017",
month = oct,
day = "1",
doi = "10.1016/j.patrec.2017.05.033",
language = "English",
volume = "97",
pages = "44--45",
journal = "Pattern Recognition Letters",
issn = "0167-8655",
publisher = "Elsevier",
}