Class "ExClust"

Description

S4 class for storing exemplar-based clusterings

Objects

Objects of this class can be created by calling cutree to cut out a clustering level from a cluster hierarchy of class AggExResult. Moreover, cutree can also be used to convert an object of class APResult to class ExClust.

Slots

The following slots are defined for ExClust objects:

l:

number of samples in the data set

sel:

subset of samples used for leveraged clustering

exemplars:

vector containing indices of exemplars

clusters:

list containing the clusters; the i-th component is a vector of indices of data points belonging to the i-th exemplar (including the exemplar itself)

idx:

vector of length l realizing a sample-to-exemplar mapping; the i-th entry contains the index of the exemplar the i-th sample belongs to

sim:

similarity matrix; only available if the preceding clustering method was called with includeSim=TRUE.

call:

method call of the preceding clustering method

Methods

plot

signature(x="ExClust"): see plot-methods

plot

signature(x="ExClust", y="matrix"): see plot-methods

heatmap

signature(x="ExClust"): see heatmap-methods

heatmap

signature(x="ExClust", y="matrix"): see heatmap-methods

show

signature(object="ExClust"): see show-methods

labels

signature(object="ExClust"): see labels-methods

cutree

signature(object="ExClust", k="ANY", h="ANY"): see cutree-methods

length

signature(x="ExClust"): gives the number of clusters.

sort

signature(x="ExClust"): see sort-methods

as.hclust

signature(x="ExClust"): see coerce-methods

as.dendrogram

signature(object="ExClust"): see coerce-methods

Accessors

In the following code snippets, x is an ExClust object.

x[[i]]: Returns the i-th cluster as a list of indices of samples belonging to the i-th cluster.

x[i]: Returns a list of integer vectors with the indices of samples belonging to this cluster. The list has as many components as the argument i has elements. A list is returned even if i is a single integer.

similarity

signature(x="ExClust"): gives the similarity matrix.

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.

See Also

aggExCluster, show-methods, plot-methods, labels-methods, cutree-methods, AggExResult, APResult

Examples

## create two Gaussian clouds
cl1 <- cbind(rnorm(20, 0.2, 0.05), rnorm(20, 0.8, 0.06))
cl2 <- cbind(rnorm(25, 0.7, 0.08), rnorm(25, 0.3, 0.05))
x <- rbind(cl1, cl2)

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

## run affinity propagation
aggres <- aggExCluster(sim)

## extract level with two clusters
excl <- cutree(aggres, k=2)

## show details of clustering results
show(excl)

## plot information about clustering run
plot(excl, x)