Flexible Models for Microclustering with Application to Entity Resolution
This addresses a specific issue in entity resolution and related fields where cluster sizes need to be sublinear, offering a tailored solution for data matching tasks.
The paper tackles the problem that standard clustering models assume cluster sizes grow linearly with data size, which is inappropriate for tasks like entity resolution where clusters should be small and independent of data set size. They introduce a new class of models with a microclustering property and show comparisons on four entity-resolution data sets.
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 applications, this assumption is inappropriate. For example, when performing entity resolution, the size of each cluster should be unrelated to the size of the data set, and each cluster should contain a negligible fraction of the total number of data points. These applications require models that yield clusters whose sizes grow sublinearly with the size of the data set. We address this requirement by defining the microclustering property and introducing a new class of models that can exhibit this property. We compare models within this class to two commonly used clustering models using four entity-resolution data sets.