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.
a list of R objects as in
cl_ensemble creates “cluster ensembles”, which are
realized as lists of clusterings (or dissimilarities) with additional
class information, always inheriting from
elements of the ensemble must have the same number of objects.
If all elements are partitions, the ensemble has class
if all elements are dendrograms, it has class
"cl_dendrogram_ensemble" and inherits from
if all elements are hierarchies (but not always dendrograms), it has
Note that empty or “mixed” ensembles cannot be categorized
according to the kind of elements they contain, and hence only have
The list representation makes it possible to use
computations on the individual clusterings in (i.e., the components
of) a cluster ensemble.
Available methods for cluster ensembles include those for
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
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)
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