They represent a partitioning of a dataset into clusters.
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:
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 one medoid.
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 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).
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-cluster iff for each observation i the maximal dissimilarity
between i and any other observation of the cluster is smaller than the minimal
dissimilarity between i and any observation of another cluster.
Clearly each L*-cluster is also an L-cluster.
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 of i (the cluster, not
containing i, for which the average dissimilarity between its observations
and i is minimal), and the silhouette width of i.
The other two components give the average silhouette width per cluster and
the average silhouette width for the dataset.
See
plot.partition for more information.
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.