标题： 微聚类：当聚类规模随数据集规模的生长次线性增长时 显示英文标题

标题： Microclustering: When the Cluster Sizes Grow Sublinearly with the Size of the Data Set

摘要： 大多数用于聚类的生成模型隐式地假设每个簇中的数据点数量随着总数据点数量线性增长。 有限混合模型、Dirichlet过程混合模型以及Pitman--Yor过程混合模型都做出了这一假设，所有其他无限可交换的聚类模型也是如此。 然而，对于某些任务，这种假设是不理想的。 例如，在执行实体解析时，每个簇的大小通常与数据集的大小无关。 因此，每个簇只包含总数据点数量的一个微不足道的比例。 因此，这些任务需要能够产生簇的模型，其大小随数据集大小以次线性方式增长。 我们通过定义\emph{小聚类性质}并引入一种具有此特性的新模型来满足这一需求。 我们通过使用真实和模拟数据集检查模型拟合度，将该模型与几种常用的聚类模型进行了比较。 显示英文摘要

摘要： 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.