flexclust (version 1.4-1)

stepFlexclust: Run Flexclust Algorithms Repeatedly

Description

Runs clustering algorithms repeatedly for different numbers of clusters and returns the minimum within cluster distance solution for each.

Usage

stepFlexclust(x, k, nrep=3, verbose=TRUE, FUN = kcca, drop=TRUE,
              group=NULL, simple=FALSE, save.data=FALSE, seed=NULL,
              multicore=TRUE, ...)
stepcclust(...)
# S4 method for stepFlexclust,missing
plot(x, y,
  type=c("barplot", "lines"), totaldist=NULL,
  xlab=NULL, ylab=NULL, …)
# S4 method for stepFlexclust
getModel(object, which=1)

Arguments

x, …

Passed to kcca or cclust.

k

A vector of integers passed in turn to the k argument of kcca

nrep

For each value of k run kcca nrep times and keep only the best solution.

FUN

Cluster function to use, typically kcca or cclust.

verbose

If TRUE, show progress information during computations.

drop

If TRUE and K is of length 1, then a single cluster object is returned instead of a "stepFlexclust" object.

group

An optional grouping vector for the data, see kcca for details.

simple

Return an object of class "kccasimple"?

save.data

Save a copy of x in the return object?

seed

If not NULL, a call to set.seed() is made before any clustering is done.

multicore

If TRUE, use mclapply() from package parallel for parallel processing.

y

Not used.

type

Create a barplot or lines plot.

totaldist

Include value for 1-cluster solution in plot? Default is TRUE if K contains 2, else FALSE.

xlab, ylab

Graphical parameters.

object

Object of class "stepFlexclust".

which

Number of model to get. If character, interpreted as number of clusters.

Details

stepcclust is a simple wrapper for stepFlexclust(...,FUN=cclust).

Examples

# NOT RUN {
data("Nclus")
plot(Nclus)

## multicore off for CRAN checks
cl1 <- stepFlexclust(Nclus, k=2:7, FUN=cclust, multicore=FALSE)
cl1

plot(cl1)

# two ways to do the same:
getModel(cl1, 4)
cl1[[4]]

opar <- par("mfrow")
par(mfrow=c(2, 2))
for(k in 3:6){
  image(getModel(cl1, as.character(k)), data=Nclus)
  title(main=paste(k, "clusters"))
}
par(opar)
# }