Random Partition Models for Microclustering Tasks release_ui55xpofunhlnmlkvcs4fgrlda

by Brenda Betancourt, Giacomo Zanella, Rebecca C. Steorts

Published in figshare.com by Taylor & Francis.



Traditional Bayesian random partition models assume that the size of each cluster grows linearly with the number of data points. While this is appealing for some applications, this assumption is not appropriate for other tasks such as entity resolution, modeling of sparse networks, and DNA sequencing tasks. Such applications require models that yield clusters whose sizes grow sublinearly with the total number of data points — the <i>microclustering property</i>. Motivated by these issues, we propose a general class of random partition models that satisfy the microclustering property with well-characterized theoretical properties. Our proposed models overcome major limitations in the existing literature on microclustering models, namely a lack of interpretability, identifiability, and full characterization of model asymptotic properties. Crucially, we drop the classical assumption of having an exchangeable sequence of data points, and instead assume an exchangeable sequence of clusters. In addition, our framework provides flexibility in terms of the prior distribution of cluster sizes, computational tractability, and applicability to a large number of microclustering tasks. We establish theoretical properties of the resulting class of priors, where we characterize the asymptotic behavior of the number of clusters and of the proportion of clusters of a given size. Our framework allows a simple and efficient Markov chain Monte Carlo algorithm to perform statistical inference. We illustrate our proposed methodology on the microclustering task of entity resolution, where we provide a simulation study and real experiments on survey panel data.
In text/plain format

Archived Files and Locations

application/pdf  806.4 kB
web.archive.org (webarchive)
s3-eu-west-1.amazonaws.com (publisher)
Read Archived PDF
Type  article-journal
Stage   published
Date   2020-10-28
Version   v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 1bfb11e9-f50e-4274-a63d-cee6ff5c041d