pam.object {cluster}  R Documentation 
The objects of class "pam"
represent a partitioning of a
dataset into clusters.
A legitimate pam
object is a list
with the following components:
medoids 
the medoids or representative objects of the
clusters. If a dissimilarity matrix was given as input to

id.med 
integer vector of indices giving the medoid observation numbers. 
clustering 
the clustering vector, see 
objective 
the objective function after the first and second
step of the 
isolation 
vector with length equal to the number of clusters, specifying which
clusters are isolated clusters (L or L*clusters) and which clusters are
not isolated. 
clusinfo 
matrix, each row gives numerical information for one cluster. These are the cardinality of the cluster (number of observations), the maximal and average dissimilarity between the observations in the cluster and the cluster's medoid, the diameter of the cluster (maximal dissimilarity between two observations of the cluster), and the separation of the cluster (minimal dissimilarity between an observation of the cluster and an observation of another cluster). 
silinfo 
list with silhouette width information, see

diss 
dissimilarity (maybe NULL), see 
call 
generating call, see 
data 
(possibibly standardized) see 
These objects are returned from pam
.
The "pam"
class has methods for the following generic functions:
print
, summary
.
The class "pam"
inherits from "partition"
.
Therefore, the generic functions plot
and clusplot
can
be used on a pam
object.
pam
, dissimilarity.object
,
partition.object
, plot.partition
.
## Use the silhouette widths for assessing the best number of clusters, ## following a onedimensional example from Christian Hennig : ## x < c(rnorm(50), rnorm(50,mean=5), rnorm(30,mean=15)) asw < numeric(20) ## Note that "k=1" won't work! for (k in 2:20) asw[k] < pam(x, k) $ silinfo $ avg.width k.best < which.max(asw) cat("silhouetteoptimal number of clusters:", k.best, "\n") plot(1:20, asw, type= "h", main = "pam() clustering assessment", xlab= "k (# clusters)", ylab = "average silhouette width") axis(1, k.best, paste("best",k.best,sep="\n"), col = "red", col.axis = "red")