Most generative models for clustering implicitly assume that the number of
data points in each cluster grows linearly with the total number of data
points. Finite mixture models, Dirichlet process mixture models, and
Pitman--Yor process mixture models make this assumption, as do all other
infinitely exchangeable clustering models. However, for some tasks, this
assumption is undesirable. For example, when performing entity resolution, the
size of each cluster is often unrelated to the size of the data set.
Consequently, each cluster contains a negligible fraction of the total number
of data points. Such tasks therefore require models that yield clusters whose
sizes grow sublinearly with the size of the data set. We address this
requirement by defining the \emph{microclustering property} and introducing a
new model that exhibits this property. We compare this model to several
commonly used clustering models by checking model fit using real and simulated
data sets.

本研究提出一种具有微聚类特性的新模型，可应用于需要聚类大小与数据集大小呈次线性增长的任务，通过比对实验数据和模拟数据的适配情况，验证该模型的有效性。