We investigate the use of clustering methods on data produced by a stochastic simulator, with applications in anomaly detection, pre-optimization, and online monitoring. We introduce an agglomerative clustering algorithm that clusters multivariate empirical distributions using the regularized Wasserstein distance and apply the proposed methodology on a call-center model.

翻译：本研究探讨了在随机仿真器生成数据上应用聚类方法，其应用场景包括异常检测、预优化与在线监控。我们提出了一种凝聚聚类算法，该算法利用正则化Wasserstein距离对多元经验分布进行聚类，并将所提方法应用于呼叫中心模型。