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, ...)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.
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.
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.
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.
logical. If TRUE, pam is
used, otherwise clara (recommended for large
datasets with 2,000 or more observations; dissimilarity matrices can
not be used with clara).
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.
numeric between 0 and 1, tuning constant for
dudahart2 (only used for 1-cluster test).
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.
logical. If TRUE, the criterion value is printed
out for every number of clusters.
passed on to distcritmulti if
criterion="multiasw".
passed on to distcritmulti if
criterion="multiasw".
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/
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.
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)
Run the code above in your browser using DataLab