diana(x, diss=F, metric="euclidean", stand=F)
x
|
data matrix or dataframe, or dissimilarity matrix, depending on the
value of the diss argument.
In case of a matrix or dataframe, each row corresponds to an observation, and each column corresponds to a variable. All variables must be numeric. Missing values (NAs) are allowed.
In case of a dissimilarity matrix,
|
diss
|
logical flag: if TRUE, then x will be considered as a dissimilarity
matrix. If FALSE, then x will be considered as a matrix of
observations by variables.
|
metric
|
character string specifying the metric to be used for calculating
dissimilarities between objects.
The currently available options are "euclidean" and "manhattan".
Euclidean distances are root sum-of-squares of differences, and
manhattan distances are the sum of absolute differences.
If x is already a dissimilarity matrix, then this argument will
be ignored.
|
stand
|
logical flag: if TRUE, then the measurements in x are standardized before
calculating the dissimilarities. Measurements are standardized for each
variable (column), by subtracting the variable's mean value and dividing by
the variable's mean absolute deviation.
If x is already a dissimilarity matrix, then this argument
will be ignored.
|
diana
is fully described in chapter 6 of Kaufman and Rousseeuw (1990).
It is probably unique in computing a divisive hierarchy, whereas most
other software for hierarchical clustering is agglomerative.
Moreover, diana
provides (a) the divisive coefficient
(see diana.object
) which measures the amount of clustering structure
found; and (b) the banner, a novel graphical display
(see plot.diana
).
The diana
-algorithm constructs a hierarchy of clusterings,
starting with one large
cluster containing all n objects. Clusters are divided until each cluster
contains only a single object.
At each stage, the cluster with the largest diameter is selected.
(The diameter of a cluster is the largest dissimilarity between any
two of its objects.)
To divide the selected cluster, the algorithm first looks for its most
disparate object (i.e., which has the largest average dissimilarity to the
other objects of the selected cluster). This object initiates the
"splinter group". In subsequent steps, the algorithm reassigns objects
that are closer to the "splinter group" than to the "old party". The result
is a division of the selected cluster into two new clusters.
"diana"
representing the clustering.
See diana.object for details.
agnes
, diana
, and
mona
construct a hierarchy of clusterings, with the number of clusters
ranging from one to the number of objects. Partitioning methods like pam
,
clara
, and fanny
require that the number of clusters be given by
the user.
diana.object
, daisy
, dist
, plot.diana
, pltree.diana
, agnes
.
dia1 <- diana(votes.repub, metric="manhattan", stand=T) print(dia1) plot(dia1) dia2 <- diana(daisy(votes.repub), diss=T, method="complete") pltree(dia2) diana(dist(votes.repub), diss=T)