Year: 2022
Type: preprint
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 $k$-means, $k$-medians, $k$-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.
Source: arXiv (Cornell University)
Author: Laber, Eduardo Sany
Institution: Pontifícia Universidade Católica do Rio de Janeiro
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4292957221