This calls the function `pam`

or
`clara`

to perform a
partitioning around medoids clustering with the number of clusters
estimated by optimum average silhouette width (see
`pam.object`

) or Calinski-Harabasz
index (`calinhara`

). The Duda-Hart test
(`dudahart2`

) is applied to decide whether there should be
more than one cluster (unless 1 is excluded as number of clusters or
data are dissimilarities).

```
pamk(data,krange=2:10,criterion="asw", usepam=TRUE,
scaling=FALSE, alpha=0.001, diss=inherits(data, "dist"),
critout=FALSE, ns=10, seed=NULL, ...)
```

data

a data matrix or data frame or something that can be
coerced into a matrix, or dissimilarity matrix or
object. See `pam`

for more information.

krange

integer vector. Numbers of clusters which are to be
compared by the average silhouette width criterion. Note: average
silhouette width and Calinski-Harabasz can't estimate number of
clusters `nc=1`

. If 1 is included, a Duda-Hart test is applied
and 1 is estimated if this is not significant.

criterion

one of `"asw"`

, `"multiasw"`

or
`"ch"`

. Determines whether average silhouette width (as given
out by `pam`

/`clara`

, or
as computed by `distcritmulti`

if `"multiasw"`

is
specified; recommended for large data sets with `usepam=FALSE`

)
or Calinski-Harabasz is applied. Note that the original
Calinski-Harabasz index is not defined for dissimilarities; if
dissimilarity data is run with `criterion="ch"`

, the
dissimilarity-based generalisation in Hennig and Liao (2013) is
used.

usepam

scaling

either a logical value or a numeric vector of length
equal to the number of variables. If `scaling`

is a numeric
vector with length equal to the number of variables, then each
variable is divided by the corresponding value from `scaling`

.
If `scaling`

is `TRUE`

then scaling is done by dividing
the (centered) variables by their root-mean-square, and if
`scaling`

is `FALSE`

, no scaling is done.

alpha

numeric between 0 and 1, tuning constant for
`dudahart2`

(only used for 1-cluster test).

diss

logical flag: if `TRUE`

(default for `dist`

or
`dissimilarity`

-objects), then `data`

will be considered
as a dissimilarity matrix (and the potential number of clusters 1
will be ignored). If `FALSE`

, then `data`

will
be considered as a matrix of observations by variables.

critout

logical. If `TRUE`

, the criterion value is printed
out for every number of clusters.

ns

passed on to `distcritmulti`

if
`criterion="multiasw"`

.

seed

passed on to `distcritmulti`

if
`criterion="multiasw"`

.

A list with components

The output of the optimal run of the
`pam`

-function.

the optimal number of clusters.

vector of criterion values for numbers of
clusters. `crit[1]`

is the p-value of the Duda-Hart test
if 1 is in `krange`

and `diss=FALSE`

.

Calinski, R. B., and Harabasz, J. (1974) A Dendrite Method for Cluster
Analysis, *Communications in Statistics*, 3, 1-27.

Duda, R. O. and Hart, P. E. (1973) *Pattern Classification and
Scene Analysis*. Wiley, New York.

Hennig, C. and Liao, T. (2013) How to find an appropriate clustering
for mixed-type variables with application to socio-economic
stratification, *Journal of the Royal Statistical Society, Series
C Applied Statistics*, 62, 309-369.

Kaufman, L. and Rousseeuw, P.J. (1990). "Finding Groups in Data: An Introduction to Cluster Analysis". Wiley, New York.

# NOT RUN { options(digits=3) set.seed(20000) face <- rFace(50,dMoNo=2,dNoEy=0,p=2) pk1 <- pamk(face,krange=1:5,criterion="asw",critout=TRUE) pk2 <- pamk(face,krange=1:5,criterion="multiasw",ns=2,critout=TRUE) # "multiasw" is better for larger data sets, use larger ns then. pk3 <- pamk(face,krange=1:5,criterion="ch",critout=TRUE) # }