flexclust: stepFlexclust – R documentation

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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 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

Flexible Cluster Algorithms

v1.4-0

GPL-2

Authors

Friedrich Leisch [aut, cre] (<https://orcid.org/0000-0001-7278-1983>), Evgenia Dimitriadou [ctb], Bettina Gruen [aut] (<https://orcid.org/0000-0001-7265-4773>)

Initial release

2018-09-20