apcluster (version 1.2.1)

apclusterK: Affinity Propagation for Pre-defined Number of Clusters

Description

Runs affinity propagation clustering for a given similarity matrix adjusting input preferences iteratively in order to achieve a desired number of clusters

Usage

apclusterK(s, K, prc=10, bimaxit=20, exact=FALSE,
           nonoise=FALSE, seed=NA, verbose=FALSE, ...)

Arguments

s

an $l\times l$ similarity matrix

K

desired number of clusters; must be between 2 and l-1

prc

the algorithm stops if the number of clusters does not deviate more than prc percent from desired value K; set to 0 if you want to have exactly K clusters

bimaxit

maximum number of bisection steps to perform; note that no warning is issued if the number of clusters is still not in the desired range

exact

flag indicating whether or not to compute the initial preference range exactly (see preferenceRange)

nonoise

apcluster adds a small amount of noise to s to prevent degenerate cases; if TRUE, this is disabled

seed

for reproducibility, the seed of the random number generator can be set to a fixed value before adding noise (see above), if NA, the seed remains unchanged

verbose

flag indicating whether status information should be displayed during bisection

...

all other arguments are passed to apcluster as they are

Value

• Upon successful completion, the function returns a APResult object.

Details

apclusterK first runs preferenceRange to determine the range of meaningful choices of the input preference p. Then it decreases p exponentially for a few iterations to obtain a good initial guess for p. If the number of clusters is still too far from the desired goal, bisection is applied.

Apart from minor adaptations and optimizations, the implementation is largely analogous to Frey's and Dueck's Matlab code (see http://www.psi.toronto.edu/affinitypropagation/).

References

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

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}.

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}.

See Also

apcluster, preferenceRange, APResult

Examples

## create three Gaussian clouds
cl1 <- cbind(rnorm(70, 0.2, 0.05), rnorm(70, 0.8, 0.06))
cl2 <- cbind(rnorm(50, 0.7, 0.08), rnorm(50, 0.3, 0.05))
cl3 <- cbind(rnorm(60, 0.8, 0.04), rnorm(60, 0.8, 0.05))
x <- rbind(cl1, cl2, cl3)

## create similarity matrix
sim <- negDistMat(x, r=2)

## run affinity propagation such that 3 clusters are obtained
apres <- apclusterK(sim, 3)

## show details of clustering results
show(apres)

## plot clustering result
plot(apres, x)