flexclust (version 0.8-0)

qtclust: 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.

Value

• An object of class "kcca".

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.

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)