qtclust {flexclust}R Documentation

QT Clustering

Description

Perform QT clustering on a data matrix.

Usage

qtclust(x, radius, family = kccaFamily("kmeans"), control = NULL)

Arguments

x A numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with all numeric columns).
radius Maximum radius of clusters.
family Object of class kccaFamily.
control An object of class flexclustControl.

Details

This function implements a generalization of the QT clustering algorithm by Heyer et al. (1999). The only difference is that in each iteration not all possible cluster start points are considered, but only a random sample of size control@ntry. In most cases the resulting solutions are almost the same at a considerable speed increase. If control@ntry is set to the size of the data set, the original algorithm is obtained.

Value

An object of class "kcca".

Author(s)

Friedrich Leisch

References

Heyer, L. J., Kruglyak, S., Yooseph, S. (1999). Exploring expression data: Identification and analysis of coexpressed genes. Genome Research 9, 1106–1115.

Examples

x <- matrix(10*runif(1000), ncol=2)

## maximum distrance of point to cluster center is 3
cl1 <- qtclust(x, radius=3)

## maximum distrance of point to cluster center is 1
## -> more clusters, longer runtime
cl2 <- qtclust(x, radius=1)

opar <- par(c("mfrow","mar"))
par(mfrow=c(2,1), mar=c(2.1,2.1,1,1))
plot(x, col=predict(cl1), xlab="", ylab="")
plot(x, col=predict(cl2), xlab="", ylab="")
par(opar)

[Package flexclust version 0.8-1 Index]