Cluster the data in x using the bagged clustering
algorithm. A partitioning cluster algorithm such as
cclust is run repeatedly on bootstrap samples from the
original data. The resulting cluster centers are then combined using
the hierarchical cluster algorithm hclust.
bclust(x, k = 2, base.iter = 10, base.k = 20, minsize = 0,
dist.method = "euclidian", hclust.method = "average",
FUN = "cclust", verbose = TRUE, final.cclust = FALSE,
resample = TRUE, weights = NULL, maxcluster = base.k, ...)
# S4 method for bclust,missing
plot(x, y, maxcluster = x@maxcluster, main = "", ...)
# S4 method for bclust,missing
clusters(object, newdata, k, ...)
# S4 method for bclust
parameters(object, k)bclust returns objects of class
"bclust" including the slots
Return value of the hierarchical clustering of the
collection of base centers (Object of class "hclust").
Vector with indices of the clusters the inputs are assigned to.
Matrix of centers of the final clusters. Only useful, if the hierarchical clustering method produces convex clusters.
Matrix of all iter.base * base.centers
centers found in the base runs.
Matrix of inputs (or object of class "bclust" for plot).
Number of clusters.
Number of runs of the base cluster algorithm.
Number of centers used in each repetition of the base method.
Minimum number of points in a base cluster.
Distance method used for the hierarchical
clustering, see dist for available distances.
Linkage method used for the hierarchical
clustering, see hclust for available methods.
Partitioning cluster method used as base algorithm.
Output status messages.
If TRUE, a final cclust step is performed
using the output of the bagged clustering as initialization.
Logical, if TRUE the base method is run on
bootstrap samples of x, else directly on x.
Vector of length nrow(x), weights for the
resampling. By default all observations have equal weight.
Maximum number of clusters memberships are to be computed for.
Object of class "bclust".
Main title of the plot.
Optional arguments top be passed to the base method
in bclust, ignored in plot.
Missing.
An optional data matrix with the same number of columns as the cluster centers. If omitted, the fitted values are used.
Friedrich Leisch
First, base.iter bootstrap samples of the original data in
x are created by drawing with replacement. The base cluster
method is run on each of these samples with base.k
centers. The base.method must be the name of a partitioning
cluster function returning an object with the same slots as the
return value of cclust.
This results in a collection of iter.base * base.centers
centers, which are subsequently clustered using the hierarchical
method hclust. Base centers with less than
minsize points in there respective partitions are removed
before the hierarchical clustering. The resulting dendrogram is
then cut to produce k clusters.
Friedrich Leisch. Bagged clustering. Working Paper 51, SFB ``Adaptive Information Systems and Modeling in Economics and Management Science'', August 1999. tools:::Rd_expr_doi("10.57938/9b129f95-b53b-44ce-a129-5b7a1168d832")
Sara Dolnicar and Friedrich Leisch. Winter tourist segments in Austria: Identifying stable vacation styles using bagged clustering techniques. Journal of Travel Research, 41(3):281-292, 2003.
hclust, cclust
data(iris)
bc1 <- bclust(iris[,1:4], 3, base.k=5)
plot(bc1)
table(clusters(bc1, k=3))
parameters(bc1, k=3)
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