• Mathematics
  • Published 2016

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

@inproceedings{Miller2016MicroclusteringWT,
  title={Microclustering: When the Cluster Sizes Grow Sublinearly with the Size of the Data Set},
  author={Jeffrey Miller and Brenda Betancourt and Abbas Zaidi and Hanna M. Wallach and Rebecca C. Steorts},
  year={2016}
}
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… CONTINUE READING

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