  Data cohesion, a recently introduced measure inspired by social interactions,
uses distance comparisons to assess relative proximity. In this work, we
provide a collection of results which can guide the development of
cohesion-based methods in exploratory data analysis and human-aided
computation. Here, we observe the important role of highly clustered
"point-like" sets and the ways in which cohesion allows such sets to take on
qualities of a single weighted point. In doing so, we see how cohesion
complements metric-adjacent measures of dissimilarity and responds to local
density. We conclude by proving that cohesion is the unique function with (i)
average value equal to one-half and (ii) the property that the influence of an
outlier is proportional to its mass. Properties of cohesion are illustrated
with examples throughout.
