apcluster (version 1.0.2)

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 adjust number of clusters to given value

Usage

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

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 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

...

all other arguments are passed to apcluster as they are

Value

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

Details

Affinity Propagation clusters data, using a set of real-valued pairwise data point similarities as input. Clusters are each represented by a cluster center data point (the exemplar). The method is iterative and searches for clusters so as to maximize an objective function, called net similarity.

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.

See Also

apcluster, preferenceRange, APResult-class

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)