diana.object {cluster}  R Documentation 
The objects of class "diana"
represent a divisive hierarchical clustering of a dataset.
A legitimate diana
object is a list with the following components:
order 
a vector giving a permutation of the original observations to allow for plotting, in the sense that the branches of a clustering tree will not cross. 
order.lab 
a vector similar to order , but containing observation labels
instead of observation numbers. This component is only available if
the original observations were labelled.

height 
a vector with the diameters of the clusters prior to splitting. 
dc 
the divisive coefficient, measuring the clustering structure of the
dataset. For each observation i, denote by d(i) the diameter of the
last cluster to which it belongs (before being split off as a single
observation), divided by the diameter of the whole dataset. The
dc is the average of all 1  d(i). It can also be seen
as the average width (or the percentage filled) of the banner plot.
Because dc grows with the number of observations, this
measure should not be used to compare datasets of very different
sizes.

merge 
an (n1) by 2 matrix, where n is the number of
observations. Row i of merge describes the split at step ni of
the clustering. If a number j in row r is negative, then the single
observation j is split off at stage nr. If j is positive, then the
cluster that will be splitted at stage nj (described by row j), is
split off at stage nr.

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 agnes . If a
dissimilarity matrix was given as input structure, then this component
is not available.

This class of objects is returned from diana
.
The "diana"
class has methods for the following generic functions:
print
, summary
, plot
.
The class "diana"
inherits from "twins"
.
Therefore, the generic function pltree
can be used on a
diana
object, and an as.hclust
method is
available.
agnes
, diana
, plot.diana
,
twins.object
.
## really see example(diana) ! Additionally: data(votes.repub) dv0 < diana(votes.repub, stand = TRUE) ## Cut into 2 groups: dv2 < cutree(as.hclust(dv0), k = 2) table(dv2) rownames(votes.repub)[dv2 == 1]