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APCluster - An R Package for Affinity Propagation Clustering

Implements Affinity Propagation clustering introduced by Frey and Dueck (2007; DOI:10.1126/science.1136800). The algorithms are largely analogous to the 'Matlab' code published by Frey and Dueck. The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results.

This is the source code repository. The package can be installed from CRAN. Further information and installation instructions are also available from http://www.bioinf.jku.at/software/apcluster/.

The package is maintained by Ulrich Bodenhofer. The package itself has grown over the years in which multiple students have contributed significant parts: Johannes Palme, Chrats Melkonian, Andreas Kothmeier, and Nikola Kostic

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Install

install.packages('apcluster')

Monthly Downloads

5,496

Version

1.4.10

License

GPL (>= 2)

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Maintainer

Ulrich Bodenhofer

Last Published

May 31st, 2022

Functions in apcluster (1.4.10)

AggExResult-class

Class "AggExResult"
APResult-class

Class "APResult"
ExClust-class

Class "ExClust"
apclusterK

Affinity Propagation for Pre-defined Number of Clusters
apclusterDemo

Affinity Propagation Demo
apclusterL

Leveraged Affinity Propagation
coerce-methods

Coercion of cluster hierarchies
conversions

Conversions Between Dense and Sparse Similarity Matrices
apcluster-package

APCluster Package
aggExCluster

Exemplar-based Agglomerative Clustering
apcluster

Affinity Propagation
preferenceRange

Determine Meaningful Ranges for Input Preferences
sort-methods

Sort clusters
plot

Plot Clustering Results
cutree-methods

Cut Out Clustering Level from Cluster Hierarchy
labels-methods

Generate label vector from clustering result
heatmap

Plot Heatmap
similarities

Methods for Computing Similarity Matrices
show-methods

Display Clustering Result Objects