stepFlexclust: Run Flexclust Algorithms Repeatedly

In flexclust: Flexible Cluster Algorithms

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 signature 'stepFlexclust,missing'
plot(x, y,
  type=c("barplot", "lines"), totaldist=NULL,
  xlab=NULL, ylab=NULL, ...)

## S4 method for signature '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).

Author(s)

Friedrich Leisch

Examples

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)

flexclust documentation built on April 8, 2022, 5:09 p.m.