| pamk {fpc} | R Documentation |
This calls the function pam to perform a
partitioning around medoids clustering with the number of clusters
estimated by optimum average silhouette width.
pamk(data,krange=2:10,scaling=FALSE, diss=inherits(data, "dist"),...)
data |
a data matrix or data frame, 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: This can't
estimate number of clusters nc=1, and therefore 1 should not be in
krange. |
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. |
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 a matrix of observations by variables. |
... |
further arguments to be transferred to
pam. |
A list with components
pamobject |
The output of the optimal run of the
pam-function. |
nc |
the optimal number of clusters. |
Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/
Kaufman, L. and Rousseeuw, P.J. (1990). "Finding Groups in Data: An Introduction to Cluster Analysis". Wiley, New York.
set.seed(20000) face <- rFace(50,dMoNo=2,dNoEy=0,p=2) pk <- pamk(face)