merge, on the flight, two adjacent clusters in order to allow better clustering scheme, if needed, and to avoid new computation of the unsupervised mode.
Usage
mergeClusters(object, whichOnes)
Arguments
object
an object of class unsupervised.
whichOnes
which clusters are needed to be merged ? Should be two adjacent ones.
## not run
## Crabs data
# data(crabs, package = "MASS")
## and more about
# ?crabs
## model : commit 4 clusters
# crabs.rufUnsupervised = unsupervised.randomUniformForest(crabs,
# categoricalvariablesidx = "all", nodesize = 5, threads = 1, clusters = 4)
## visualize clusters and merge adjacent clusters
# plot(crabs.rufUnsupervised)
## we can first merge clusters 1 and 4
## note that clusters may change if run again
# crabs.rufUnsupervisedNew = mergeClusters(crabs.rufUnsupervised, c(1,4))
## one can assess the fitting, comparing old and new model
# crabs.rufUnsupervised
# crabs.rufUnsupervisedNew
## visualize new model
# plot(crabs.rufUnsupervisedNew)
## merge new clusters 1 and 2 and look if it will be better
# crabs.rufUnsupervisedNewest = mergeClusters(crabs.rufUnsupervisedNew, c(1,2))
# crabs.rufUnsupervisedNewest
# plot(crabs.rufUnsupervisedNewest)
## NOTE : mergeClusters() provides choice on how to choose and assess clusters
## using simply visualization.