cl_medoid: Medoid Partitions and Hierarchies

Description Usage Arguments Details Value References See Also Examples

View source: R/medoid.R

Description

Compute the medoid of an ensemble of partitions or hierarchies, i.e., the element of the ensemble minimizing the sum of dissimilarities to all other elements.

Usage

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cl_medoid(x, method = "euclidean")

Arguments

x

an ensemble of partitions or hierarchies, or something coercible to that (see cl_ensemble).

method

a character string or a function, as for argument method of function cl_dissimilarity.

Details

Medoid clusterings are special cases of “consensus” clusterings characterized as the solutions of an optimization problem. See Gordon (2001) for more information.

The dissimilarities d for determining the medoid are obtained by calling cl_dissimilarity with arguments x and method. The medoid can then be found as the (first) row index for which the row sum of as.matrix(d) is minimal. Modulo possible differences in the case of ties, this gives the same results as (the medoid obtained by) pam in package cluster.

Value

The medoid partition or hierarchy.

References

A. D. Gordon (1999). Classification (2nd edition). Boca Raton, FL: Chapman & Hall/CRC.

See Also

cl_consensus

Examples

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## An ensemble of partitions.
data("CKME")
pens <- CKME[1 : 20]
m1 <- cl_medoid(pens)
diss <- cl_dissimilarity(pens)
require("cluster")
m2 <- pens[[pam(diss, 1)$medoids]]
## Agreement of medoid consensus partitions.
cl_agreement(m1, m2)
## Or, more straightforwardly:
table(cl_class_ids(m1), cl_class_ids(m2))

clue documentation built on April 23, 2018, 5:04 p.m.