pamk: Partitioning around medoids with estimation of number of clusters

Description

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).

Usage

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

Arguments

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 tes

criterion

one of "asw" or "ch". Determines whether average silhouette width or Calinski-Harabasz is applied.

usepam

logical. If TRUE, pam is used, otherwise clara (recommended for large datasets with 2,000 or more observations; dissimilarity ma

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

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. If FALSE, then data will be considered as

critout

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

...

further arguments to be transferred to pam or clara.

Value

A list with components
pamobjectThe output of the optimal run of the pam-function.
ncthe optimal number of clusters.

References

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.

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

Examples

Run this code

set.seed(20000)
  face <- rFace(50,dMoNo=2,dNoEy=0,p=2)
  pk <- pamk(face,krange=1:5,criterion="asw",critout=TRUE)
  pk <- pamk(face,krange=1:5,criterion="ch",critout=TRUE)