| 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)