ensemble {clue} | R Documentation |
Creation and manipulation of cluster ensembles.
cl_ensemble(..., list = NULL) as.cl_ensemble(x) is.cl_ensemble(x)
... |
R objects representing clusterings of the same kind (either all partitions or all hierarchies) on the same objects. |
list |
a list of R objects as in ... . |
x |
for as.cl_ensemble , an R object as in ... ;
for is.cl_ensemble , an arbitrary R object. |
cl_ensemble
creates cluster ensembles, which are realized as
lists of clusterings with additional class information. All
clusterings in an ensemble must be of the same kind, and have the same
number of objects. If all clusterings are partitions, this list has
class "cl_partition_ensemble"
and inherits from
"cl_ensemble"
; if all clusterings are hierarchies, it has class
"cl_hierarchy_ensemble"
and inherits from
"cl_ensemble"
. Empty ensembles
cannot be categorized according to the kind of clusterings 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
.
Note that (currently), as.cl_ensemble
assumes that unclassed
lists represent single clusterings, as this in particular holds
true for kmeans
in versions of R prior to 2.1.0. This
may change eventually.
cl_ensemble
returns a list of the given clusterings, with
additional class information (see Details).
d <- dist(USArrests) hclust_methods <- c("ward", "single", "complete", "average", "mcquitty", "median", "centroid") hclust_results <- lapply(hclust_methods, function(m) hclust(d, m)) ## Now create an ensemble from the results. hens <- cl_ensemble(list = hclust_results) names(hens) <- hclust_methods hens ## Subscripting. hens[1 : 3] ## Replication. rep(hens, 3) ## And continue to analyze the ensemble, e.g. cl_dissimilarity(hens, method = "gamma")