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Most generative models for clustering implicitly assume that the number ofdata points in each cluster grows linearly with the total number of datapoints. Finite mixture models, Dirichlet process mixture models, andPitman--Yor process mixture models make this assumption, as do all otherinfinitely exchangeable clustering models. However, for some applications, thisassumption is inappropriate. For example, when performing entity resolution,the size of each cluster should be unrelated to the size of the data set, andeach cluster should contain a negligible fraction of the total number of datapoints. These applications require models that yield clusters whose sizes growsublinearly with the size of the data set. We address this requirement bydefining the microclustering property and introducing a new class of modelsthat can exhibit this property. We compare models within this class to twocommonly used clustering models using four entity-resolution data sets.

Flexible Models for Microclustering with Application to Entity Resolution