Skip to yearly menu bar Skip to main content


Flexible Models for Microclustering with Application to Entity Resolution

Brenda Betancourt · Giacomo Zanella · Jeffrey Miller · Hanna Wallach · Abbas Zaidi · Beka Steorts

Area 5+6+7+8 #123

Keywords: [ (Other) Bayesian Inference ] [ Bayesian Nonparametrics ] [ (Other) Statistics ] [ (Other) Unsupervised Learning Methods ]


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.

Live content is unavailable. Log in and register to view live content