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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

3,156

Version

1.4.7

License

GPL (>= 2)

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Maintainer

Ulrich Bodenhofer

Last Published

May 29th, 2018

Functions in apcluster (1.4.7)

coerce-methods

Coercion of cluster hierarchies
sort-methods

Sort clusters
labels-methods

Generate label vector from clustering result
apclusterDemo

Affinity Propagation Demo
apclusterK

Affinity Propagation for Pre-defined Number of Clusters
APResult-class

Class "APResult"
ExClust-class

Class "ExClust"
apcluster-package

APCluster Package
AggExResult-class

Class "AggExResult"
aggExCluster

Exemplar-based Agglomerative Clustering
apcluster

Affinity Propagation
apclusterL

Leveraged Affinity Propagation
apcluster-deprecated

Deprecated functions in package ‘apcluster’
conversions

Conversions Between Dense and Sparse Similarity Matrices
show-methods

Display Clustering Result Objects
heatmap

Plot Heatmap
plot

Plot Clustering Results
ch22Promoters

ch22Promoters Data Set
preferenceRange

Determine Meaningful Ranges for Input Preferences
similarities

Methods for Computing Similarity Matrices
cutree-methods

Cut Out Clustering Level from Cluster Hierarchy