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hcE(data, partition, minclus=1, ...)
hcV(data, partition, minclus = 1, alpha = 1, ...)
hcEII(data, partition, minclus = 1, ...)
hcVII(data, partition, minclus = 1, alpha = 1, ...)
hcEEE(data, partition, minclus = 1, ...)
hcVVV(data, partition, minclus = 1, alpha = 1, beta = 1, ...)
data
. If provided, group merges will
start with this partition. Otherwise, each observation is assumed to
be in a cluster by itself at the start of ado.call
call."EEE"
,
do not admit a fast algorithm under the usual agglomerative
hierachical clustering paradigm.
These use less memory but are much slower to execute.hc
,
hclass
data(iris)
irisMatrix <- as.matrix(iris[,1:4])
hcTree <- hcEII(data = irisMatrix)
cl <- hclass(hcTree,c(2,3))
par(pty = "s", mfrow = c(1,1))
clPairs(irisMatrix,cl=cl[,"2"])
clPairs(irisMatrix,cl=cl[,"3"])
par(mfrow = c(1,2))
dimens <- c(1,2)
coordProj(irisMatrix, classification=cl[,"2"], dimens=dimens)
coordProj(irisMatrix, classification=cl[,"3"], dimens=dimens)
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