R: Medoid Partitions and Hierarchies

cl_medoid {clue} | R Documentation

## 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`.

### 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

    ## 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))

[Package clue version 0.3-54 Index]