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.

翻译：多数组群的基因化模型暗含地假定,每个组群的数据点数随着数据点总数而线性增长。 精密混合模型、 狄里赫莱工艺混合模型和 Pitman- Yor工艺混合模型和所有其他无限可交换的组群模型一样,得出这一假设。 但是,对于某些任务来说,这一假设是不可取的。 例如,执行实体的分辨率,每个组群的大小往往与数据集的大小不相干。 因此,每个组群包含的数据点总数中微不足道的一小部分。 因此,这些任务需要产生组群的模型,其大小随着数据集的大小而亚线性增长。 我们通过定义 \ emph{ 微观组群属性 和引入显示这一属性的新模型来满足这一要求。 我们通过使用真实和模拟数据集来检查适合模型,将这一模型与几个常用的组群集模型进行比较。