The objects of class "partition" represent a partitioning of a
dataset into clusters.
a "partition" object is a list with the following
(and typically more) components:
the clustering vector. An integer vector of length \(n\), the number of observations, giving for each observation the number (`id') of the cluster to which it belongs.
the matched call generating the object.
a list with all silhouette information, only available when
the number of clusters is non-trivial, i.e., \(1 < k < n\) and
then has the following components, see silhouette
an (n x 3) matrix, as returned by
silhouette(), 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 \(s(i)\) of the observation.
the average silhouette width per cluster.
the average silhouette width for the dataset, i.e., simply the average of \(s(i)\) over all observations \(i\).
plot.partition. Note that avg.width can be maximized over different
clusterings (e.g. with varying number of clusters) to choose an
optimal clustering.
value of criterion maximized during the partitioning algorithm, may more than one entry for different stages.
an object of class "dissimilarity", representing the total
dissimilarity matrix of the dataset (or relevant subset, e.g. for
clara).
a matrix containing the original or standardized data. This might be missing to save memory or when a dissimilarity matrix was given as input structure to the clustering method.
These objects are returned from pam, clara or fanny.
The "partition" class has a method for the following generic functions:
plot, clusplot.
The following classes inherit from class "partition" :
"pam", "clara" and "fanny".
See pam.object, clara.object and
fanny.object for details.