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：在本文中，我们提出了一个概括最近由Berenhaut，Moore和Melvin [国家科学院院刊，119（4）（2022）]引入的凝聚概念的方法。所提出的公式建立在局部相关性和支持分割这两个关键概率概念上。先前的结果在新的背景下得到扩展，并包括在具有不确定性的数据中揭示社区的应用示例。