# NOT RUN {
## 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.
# }
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