So-called "classification trimmed likelihood curves" have been proposed as a useful heuristic tool to determine the number of clusters and trimming proportion in trimming-based robust clustering methods. However, these curves needs a careful visual inspection, and this way of choosing parameters requires subjective decisions. This work is intended to provide theoretical background for the understanding of these curves and the elements involved in their derivation. Moreover, a parametric bootstrap approach is presented in order to automatize the choice of parameter more by providing a reduced list of "sensible" choices for the parameters. The user can then pick a solution that fits their aims from that reduced list.

翻译：所谓的"分类修剪似然曲线"已被提出作为一种有用的启发式工具，用于确定基于修剪的稳健聚类方法中的聚类数量和修剪比例。然而，这些曲线需要仔细的视觉检查，这种参数选择方式依赖于主观决策。本文旨在为理解这些曲线及其推导所涉及的要素提供理论基础。此外，本文提出了一种参数自举方法，通过提供缩减的"合理"参数选择列表，实现参数选择的自动化。用户随后可从该缩减列表中选择符合其目标的解决方案。