In this paper we provide a generalization of the concept of cohesion as introduced recently by Berenhaut, Moore and Melvin [Proceedings of the National Academy of Sciences, 119 (4) (2022)]. The formulation presented builds on the technique of partitioned local depth by distilling two key probabilistic concepts: local relevance and support division. Earlier results are extended within the new context, and examples of applications to revealing communities in data with uncertainty are included.

翻译：本文对Berenhaut、Moore和Melvin近期提出的内聚性概念（见《美国国家科学院院刊》119卷4期，2022年）进行了推广。我们提出的公式基于局部划分深度技术，通过提炼两个关键概率概念——局部相关性和支撑划分——来构建。在新框架下扩展了先前的结果，并给出了在不确定性数据中揭示社区结构的应用实例。