# An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance

arXiv: https://arxiv.org/abs/2407.12100 · HTML: https://mlsanity.com/paper/2407.12100/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://mlsanity.com/paper/1705.09634/) (2017)
- [Fast Computation of Wasserstein Barycenters](https://mlsanity.com/paper/1310.4375/) (2013)
- [Wasserstein K-means for clustering probability distributions](https://mlsanity.com/paper/2209.06975/) (2022)
- [Robust clustering tools based on optimal transportation](https://mlsanity.com/paper/1607.01179/) (2016)
- [Efficient Learning for Clustering and Optimizing Context-Dependent Designs](https://mlsanity.com/paper/2012.05591/) (2020)
- [Clustering Market Regimes using the Wasserstein Distance](https://mlsanity.com/paper/2110.11848/) (2021)
- [Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection](https://mlsanity.com/paper/2402.02196/) (2024)
- [The geometry of financial institutions -- Wasserstein clustering of financial data](https://mlsanity.com/paper/2305.03565/) (2023)

## Built on by (most cited first, 1 of 1)

- [Boosted Distributional Reinforcement Learning: Analysis and Healthcare Applications](https://mlsanity.com/paper/2604.04334/) (2026)

## Abstract (arXiv)

Using statistical learning methods to analyze stochastic simulation outputs can significantly enhance decision-making by uncovering relationships between different simulated systems and between a system's inputs and outputs. We focus on clustering multivariate empirical distributions of simulation outputs to identify patterns and trade-offs among performance measures. We present a novel agglomerative clustering algorithm that utilizes the regularized Wasserstein distance to cluster these multivariate empirical distributions. This framework has several important use cases, including anomaly detection, pre-optimization, and online monitoring. In numerical experiments involving a call-center model, we demonstrate how this methodology can identify staffing plans that yield similar performance outcomes and inform policies for intervening when queue lengths signal potentially worsening system performance.