canonicalize_cluster_labels

The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) <doi:10.1080/10618600.2022.2069779>.

David B Dahl

salso

Search Algorithms and Loss Functions for Bayesian Clustering

David B. Dahl

Devin J. Johnson

Peter Müller

Andrés Felipe Barrientos

Garritt Page

David Dunson

Alex Crichton

Brendan Zabarauskas

David Tolnay

Jim Turner

Josh Stone

R. Janis Goldschmidt

Sean McArthur

Stefan Lankes

The Cranelift Project Developers 

The CryptoCorrosion Contributors 

The Rand Project Developers 

The Rust Project Developers 

Ulrik Sverdrup "bluss"

bluss 

canonicalize_cluster_labels function

<dl><dt>x</dt>
<dd>A vector of cluster labels.</dd></dl>

Arguments

Canonicalize Cluster Labels — canonicalize_cluster_labels

<dl>

<dt>x</dt>
<dd>A vector of cluster labels.</dd>

</dl>

Canonicalize Cluster Labels

canonicalize_cluster_labels: Canonicalize Cluster Labels

Description

Usage

Value

Arguments