The computational complexity of some explainable clustering problems
Machine Learning
2022-08-23 v1 Data Structures and Algorithms
Abstract
We study the computational complexity of some explainable clustering problems in the framework proposed by [Dasgupta et al., ICML 2020], where explainability is achieved via axis-aligned decision trees. We consider the -means, -medians, -centers and the spacing cost functions. We prove that the first three are hard to optimize while the latter can be optimized in polynomial time.
Cite
@article{arxiv.2208.09643,
title = {The computational complexity of some explainable clustering problems},
author = {Eduardo Sany Laber},
journal= {arXiv preprint arXiv:2208.09643},
year = {2022}
}
Comments
14 pages and 1 figure