cluster.magazine | R Documentation |
Runs a user-specified set of clustering methods (CBI-functions, see
kmeansCBI
with several numbers of clusters on a dataset
with unified output.
cluster.magazine(data,G,diss = inherits(data, "dist"),
scaling=TRUE, clustermethod,
distmethod=rep(TRUE,length(clustermethod)),
ncinput=rep(TRUE,length(clustermethod)),
clustermethodpars,
trace=TRUE)
data |
data matrix or |
G |
vector of integers. Numbers of clusters to consider. |
diss |
logical. If |
scaling |
either a logical or a numeric vector of length equal to
the number of columns of |
clustermethod |
vector of strings specifying names of
CBI-functions (see |
distmethod |
vector of logicals, of the same length as
|
ncinput |
vector of logicals, of the same length as
|
clustermethodpars |
list of the same length as
|
trace |
logical. If |
List of lists comprising
output |
Two-dimensional list. The first list index i is the number
of the clustering method (ordering as specified in
|
clustering |
Two-dimensional list. The first list index i is the number
of the clustering method (ordering as specified in
|
noise |
Two-dimensional list. The first list index i is the number
of the clustering method (ordering as specified in
|
othernc |
list of integer vectors of length 2. The first number is
the number of the clustering method (the order is determined by
argument |
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/
Hennig, C. (2017) Cluster validation by measurement of clustering characteristics relevant to the user. In C. H. Skiadas (ed.) Proceedings of ASMDA 2017, 501-520, https://arxiv.org/abs/1703.09282
clusterbenchstats
, kmeansCBI
set.seed(20000)
options(digits=3)
face <- rFace(10,dMoNo=2,dNoEy=0,p=2)
clustermethod=c("kmeansCBI","hclustCBI","hclustCBI")
# A clustering method can be used more than once, with different
# parameters
clustermethodpars <- list()
clustermethodpars[[2]] <- clustermethodpars[[3]] <- list()
clustermethodpars[[2]]$method <- "complete"
clustermethodpars[[3]]$method <- "average"
cmf <- cluster.magazine(face,G=2:3,clustermethod=clustermethod,
distmethod=rep(FALSE,3),clustermethodpars=clustermethodpars)
print(str(cmf))
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