R: Compute 'pam'-consistent Medoids from Clustering

medoids {cluster}

R Documentation

Compute `pam`-consistent Medoids from Clustering

Description

Given a data matrix or dissimilarity x for say n observational units and a clustering, compute the pam()-consistent medoids.

Usage

medoids(x, clustering, diss = inherits(x, "dist"), USE.NAMES = FALSE, ...)

Arguments

`x`	Either a data matrix or data frame, or dissimilarity matrix or object, see also `pam`.
`clustering`	an integer vector of length `n`, the number of observations, giving for each observation the number ('id') of the cluster to which it belongs. In other words, `clustering` has values from `1:k` where `k` is the number of clusters, see also `partition.object` and `cutree()`, for examples where such clustering vectors are computed.
`diss`	see also `pam`.
`USE.NAMES`	a logical, typical false, passed to the `vapply()` call computing the medoids.
`...`	optional further argument passed to `pam(xj, k=1, ...)`, notably `metric`, or `variant="f_5"` to use a faster algorithm, or `trace.lev = k`.

Value

a numeric vector of length

Author(s)

Martin Maechler, after being asked how pam() could be used instead of kmeans(), starting from a previous clustering.

Examples

## From example(agnes):
data(votes.repub)
agn1 <- agnes(votes.repub, metric = "manhattan", stand = TRUE)
agn2 <- agnes(daisy(votes.repub), diss = TRUE, method = "complete")
agnS <- agnes(votes.repub, method = "flexible", par.meth = 0.625)

for(k in 2:11) {
  print(table(cl.k <- cutree(agnS, k=k)))
  stopifnot(length(cl.k) == nrow(votes.repub), 1 <= cl.k, cl.k <= k, table(cl.k) >= 2)
  m.k <- medoids(votes.repub, cl.k)
  cat("k =", k,"; sort(medoids) = "); dput(sort(m.k), control={})
}

[Package cluster version 2.1.6 Index]

Compute pam-consistent Medoids from Clustering