Arguments
medoids
the medoids or representative objects of the clusters. If a dissimilarity
matrix was given as input to pam, then a vector of numbers or labels of
observations is given, else medoids is a matrix with in each row the
coordinates of
clustering
the clustering vector. A vector with length equal to the number of
observations, giving the number of the cluster to which each observation
belongs.
objective
the objective function after the first and second step of the pam
algorithm.
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
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.
A cluster is an L*-cluster iff its diameter is smaller than its separation.
A cluster is an L-clus
silinfo
list with all information necessary to construct a silhouette plot of the
clustering. This list is only available when 1 < k < n.
The first component is a matrix, with for each observation i the cluster to
which i belongs, as well as the neighbor cluster
diss
an object of class "dissimilarity", representing the total dissimilarity
matrix of the dataset.
data
a matrix containing the original or standardized measurements, depending
on the stand option of the function pam. If a dissimilarity matrix was
given as input structure, then this component is not available.
GENERATION
This class of objects is returned from pam.METHODS
The "pam" class has methods for the following generic functions:
print, summary.INHERITANCE
The class "pam" inherits from "partition".
Therefore, the generic functions plot and clusplot can be used on a
pam object.STRUCTURE
A legitimate pam object is a list with the following components: