Partitioning Around Medoids Object
DESCRIPTION:
These are objects of class "pam" 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". By that, the generic function plot can be used with a pam object.

STRUCTURE:
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 pam, then a vector of numbers or labels of objects 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 objects, giving the number of the cluster to which each object 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 objects), the maximal and average dissimilarity between the objects in the cluster and the cluster's medoid, the diameter of the cluster (maximal dissimilarity between two objects of the cluster), and the separation of the cluster (minimal dissimilarity between an object of the cluster and an object 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 object i the maximal dissimilarity between i and any other object of the cluster is smaller than the minimal dissimilarity between i and any object 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. The first component is a matrix, with for each object 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 objects 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.
SEE ALSO:
dissimilarity.object , pam , partition.object , plot.partition .