diana.diana(x, diss = inherits(x, "dist"), metric = "euclidean", stand = FALSE,
keep.diss = n < 100, keep.data = !diss)diss argument.In case of a matrix or data frame, each row corresponds to an observation, and each column corresponds to a variable. All
dist or
dissimilarity objects), then x will be considered as a
dissimilarity matrix. If FALSE, then x will be considered as
a matrix of observations by varx 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 mex should be kept in the result. Setting
these to FALSE can give much smaller results and hence even save
memory allocation time."diana" representing the clustering;
this class has methods for the following generic functions:
print, summary, plot. Further, 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.
A legitimate diana object is a list with the following components:
order, but containing observation labels
instead of observation numbers. This component is only available if
the original observations were labelled.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 describes the split at step n-i of
the clustering. If a number $j$ in row r is negative, then the single
observation $|j|$ is split off at stage n-r. If j is positive, then the
cluster that will be splitted at stage n-j (described by row j), is
split off at stage n-r."dissimilarity", representing the total
dissimilarity matrix of the dataset.stand option of the function agnes. If a
dissimilarity matrix was given as input structure, then this component
is not available.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 observations. Clusters are divided until each cluster
contains only a single observation.
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 observations.)
To divide the selected cluster, the algorithm first looks for its most
disparate observation (i.e., which has the largest average dissimilarity to the
other observations of the selected cluster). This observation initiates the
"splinter group". In subsequent steps, the algorithm reassigns observations
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.
agnes also for background and references;
cutree (and as.hclust) for grouping
extraction; daisy, dist,
plot.diana, twins.object.data(votes.repub)
dv <- diana(votes.repub, metric = "manhattan", stand = TRUE)
print(dv)
plot(dv)
## Cut into 2 groups:
dv2 <- cutree(as.hclust(dv), k = 2)
table(dv2) # 8 and 42 group members
rownames(votes.repub)[dv2 == 1]
## For two groups, does the metric matter ?
dv0 <- diana(votes.repub, stand = TRUE) # default: Euclidean
dv.2 <- cutree(as.hclust(dv0), k = 2)
table(dv2 == dv.2)## identical group assignments
data(agriculture)
## Plot similar to Figure 8 in ref
plot(diana(agriculture), ask = TRUE)
<testonly>plot(diana(agriculture))</testonly>Run the code above in your browser using DataLab