apcluster (version 1.2.1)

apcluster-package: APCluster Package

Description

The apcluster package implements affinity propagation according to Frey and Dueck and a method for exemplar-based agglomerative clustering. It further offers various functions for plotting clustering results.

Details

The central function is apcluster. It runs affinity propagation on a given similarity matrix. The function returns an APResult object from which the clustering itself and information about the affinity propagation run can be obtained. The package further implements an exemplar-based agglomerative clustering method aggExCluster that can be used for computing a complete cluster hierarchy, but also for joining fine-grained clusters previously obtained by affinity propagation clustering. Further functions are implemented to visualize the results and to create distance matrices.

References

http://www.bioinf.jku.at/software/apcluster

Bodenhofer, U., Kothmeier, A., and Hochreiter, S. (2011) APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463-2464. DOI: http://dx.doi.org/10.1093/bioinformatics/btr406{10.1093/bioinformatics/btr406}.

Frey, B. J. and Dueck, D. (2007) Clustering by passing messages between data points. Science 315, 972-976. DOI: http://dx.doi.org/10.1126/science.1136800{10.1126/science.1136800}.

Examples

## create two Gaussian clouds
cl1 <- cbind(rnorm(100,0.2,0.05),rnorm(100,0.8,0.06))
cl2 <- cbind(rnorm(50,0.7,0.08),rnorm(50,0.3,0.05))
x <- rbind(cl1,cl2)

## compute similarity matrix (negative squared Euclidean)
sim <- negDistMat(x, r=2)

## run affinity propagation
apres <- apcluster(sim, details=TRUE)

## show details of clustering results
show(apres)

## plot information about clustering run
plot(apres)

## plot clustering result
plot(apres, x)

## employ agglomerative clustering to join clusters
aggres <- aggExCluster(sim, apres)

## show information
show(aggres)
show(cutree(aggres, 2))

## plot dendrogram
plot(aggres)

## plot clustering result for k=2 clusters
plot(aggres, x, k=2)

## plot heatmap
plot(apres, sim)