Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Microclustering: When the Cluster Sizes Grow Sublinearly with the Size
  of the Data Set   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 the data set.
Consequently, each cluster contains a negligible fraction of the total number
of data points. Such tasks therefore require models that yield clusters whose
sizes grow sublinearly with the size of the data set. We address this
requirement by defining the \emph{microclustering property} and introducing a
new model that exhibits this property. We compare this model to several
commonly used clustering models by checking model fit using real and simulated
data sets.

11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
21
The categories of this paper are: adap-org