apcluster-package {apcluster} | R Documentation

## 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 or it creates a similarity matrix for a given data set and similarity measure and runs affinity propagation on this matrix. The function returns an `APResult` object from which the clustering itself and information about the affinity propagation run can be obtained. Leveraged affinity propagation clustering `apclusterL` allows efficient clustering of large datasets by using only a subset of the similarities. 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.

### Author(s)

Ulrich Bodenhofer, Andreas Kothmeier & Johannes Palme apcluster@bioinf.jku.at

### 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: 10.1093/bioinformatics/btr406.

Frey, B. J. and Dueck, D. (2007) Clustering by passing messages between data points. Science 315, 972-976. DOI: 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(100, 0.7, 0.08), rnorm(100, 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
    heatmap(apres, sim)

    ## leveraged apcluster
    apresL <- apclusterL(s=negDistMat(r=2), x=x,  frac=0.2, sweeps=3)

    ## show details of clustering results
    show(apresL)

    ## plot clustering result
    plot(apresL, x)

[Package apcluster version 1.4.7 Index]