Cluster Ensembles
Creation and manipulation of cluster ensembles.
cl_ensemble(..., list = NULL) as.cl_ensemble(x) is.cl_ensemble(x)
... |
R objects representing clusterings of or dissimilarities between the same objects. |
list |
a list of R objects as in |
x |
for |
cl_ensemble creates “cluster ensembles”, which are
realized as lists of clusterings (or dissimilarities) with additional
class information, always inheriting from "cl_ensemble". All
elements of the ensemble must have the same number of objects.
If all elements are partitions, the ensemble has class
"cl_partition_ensemble";
if all elements are dendrograms, it has class
"cl_dendrogram_ensemble" and inherits from
"cl_hierarchy_ensemble";
if all elements are hierarchies (but not always dendrograms), it has
class "cl_hierarchy_ensemble".
Note that empty or “mixed” ensembles cannot be categorized
according to the kind of elements they contain, and hence only have
class "cl_ensemble".
The list representation makes it possible to use lapply for
computations on the individual clusterings in (i.e., the components
of) a cluster ensemble.
Available methods for cluster ensembles include those for
subscripting, c, rep, and print. There is also a
plot method for ensembles for which all elements can be plotted
(currently, additive trees, dendrograms and ultrametrics).
cl_ensemble returns a list of the given clusterings or
dissimilarities, with additional class information (see
Details).
d <- dist(USArrests)
hclust_methods <-
c("ward", "single", "complete", "average", "mcquitty")
hclust_results <- lapply(hclust_methods, function(m) hclust(d, m))
names(hclust_results) <- hclust_methods
## Now create an ensemble from the results.
hens <- cl_ensemble(list = hclust_results)
hens
## Subscripting.
hens[1 : 3]
## Replication.
rep(hens, 3)
## Plotting.
plot(hens, main = names(hens))
## And continue to analyze the ensemble, e.g.
round(cl_dissimilarity(hens, method = "gamma"), 4)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.