clue (version 0.3-57)

cl_medoid: Medoid Partitions and Hierarchies

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

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.

Value

The medoid partition or hierarchy.

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.

References

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

See Also

cl_consensus

Examples

# NOT RUN {
## 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))
# }

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