qtclust {flexclust} | R Documentation |
Perform QT clustering on a data matrix.
qtclust(x, radius, family = kccaFamily("kmeans"), control = NULL)
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 . |
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.
An object of class "kcca"
.
Friedrich Leisch
Heyer, L. J., Kruglyak, S., Yooseph, S. (1999). Exploring expression data: Identification and analysis of coexpressed genes. Genome Research 9, 1106–1115.
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)