OneBatchPAM: A Fast and Frugal K-Medoids Algorithm
Abstract
This paper proposes a novel k-medoids approximation algorithm to handle large-scale datasets with reasonable computational time and memory complexity. We develop a local-search algorithm that iteratively improves the medoid selection based on the estimation of the k-medoids objective. A single batch of size m << n provides the estimation, which reduces the required memory size and the number of pairwise dissimilarities computations to O(mn), instead of O(n^2) compared to most k-medoids baselines. We obtain theoretical results highlighting that a batch of size m = O(log(n)) is sufficient to guarantee, with strong probability, the same performance as the original local-search algorithm. Multiple experiments conducted on real datasets of various sizes and dimensions show that our algorithm provides similar performances as state-of-the-art methods such as FasterPAM and BanditPAM++ with a drastically reduced running time.
Cite
@article{arxiv.2501.19285,
title = {OneBatchPAM: A Fast and Frugal K-Medoids Algorithm},
author = {Antoine de Mathelin and Nicolas Enrique Cecchi and François Deheeger and Mathilde Mougeot and Nicolas Vayatis},
journal= {arXiv preprint arXiv:2501.19285},
year = {2025}
}
Comments
Paper accepted by AAAI 2025