partition.object: Partitioning Object
Description
The objects of class "partition" represent a partitioning of a
dataset into clusters.
Value
"partition" object is a list with the following
(and typically more) components:
- clustering
-
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.
- call
- the matched
call generating the object. - silinfo
-
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 $i$.
This information is also needed to construct a silhouette plot of
the clustering, see plot.partition.Note that avg.width can be maximized over different
clusterings (e.g. with varying number of clusters) to choose an
optimal clustering.
- objective
- value of criterion maximized during the
partitioning algorithm, may more than one entry for different stages.
- diss
-
an object of class
"dissimilarity", representing the total
dissimilarity matrix of the dataset (or relevant subset, e.g. for
clara).
- data
-
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.
GENERATION
These objects are returned from pam, clara or fanny.METHODS
The "partition" class has a method for the following generic functions:
plot, clusplot.