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`

- widths
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.- clus.avg.widths
the average silhouette width per cluster.

- avg.width
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.