These functions provide an interface to several clustering methods
implemented in R, for use together with the cluster stability
assessment in clusterboot (as parameter
clustermethod; "CBI" stands for "clusterboot interface").
In some situations it could make sense to use them to compute a
clustering even if you don't want to run clusterboot, because
some of the functions contain some additional features (e.g., normal
mixture model based clustering of dissimilarity matrices projected
into the Euclidean space by MDS or partitioning around medoids with
estimated number of clusters, noise/outlier identification in
hierarchical clustering).
kmeansCBI(data,krange,k,scaling=FALSE,runs=1,criterion="ch",...)hclustCBI(data,k,cut="number",method,scaling=TRUE,noisecut=0,...)
hclusttreeCBI(data,minlevel=2,method,scaling=TRUE,...)
disthclustCBI(dmatrix,k,cut="number",method,noisecut=0,...)
noisemclustCBI(data,G,k,modelNames,nnk,hcmodel=NULL,Vinv=NULL,
summary.out=FALSE,...)
distnoisemclustCBI(dmatrix,G,k,modelNames,nnk,
hcmodel=NULL,Vinv=NULL,mdsmethod="classical",
mdsdim=4, summary.out=FALSE, points.out=FALSE,...)
claraCBI(data,k,usepam=TRUE,diss=inherits(data,"dist"),...)
pamkCBI(data,krange=2:10,k=NULL,criterion="asw", usepam=TRUE,
scaling=FALSE,diss=inherits(data,"dist"),...)
tclustCBI(data,k,trim=0.05,...)
dbscanCBI(data,eps,MinPts,diss=inherits(data,"dist"),...)
mahalCBI(data,clustercut=0.5,...)
mergenormCBI(data, G=NULL, k=NULL, modelNames=NULL, nnk=0,
hcmodel = NULL,
Vinv = NULL, mergemethod="bhat",
cutoff=0.1,...)
speccCBI(data,k,...)
pdfclustCBI(data,...)
stupidkcentroidsCBI(dmatrix,k,distances=TRUE)
stupidknnCBI(dmatrix,k)
stupidkfnCBI(dmatrix,k)
stupidkavenCBI(dmatrix,k)
All interface functions return a list with the following components
(there may be some more, see summary.out and points.out
above):
clustering result, usually a list with the full output of the clustering method (the precise format doesn't matter); whatever you want to use later.
number of clusters. If some points don't belong to any
cluster, these are declared "noise". nc includes the
"noise cluster", and there should be another component
nccl, being the number of clusters not including the
noise cluster.
this is a list consisting of a logical vectors
of length of the number of data points (n) for each cluster,
indicating whether a point is a member of this cluster
(TRUE) or not. If a noise cluster is included, it
should always be the last vector in this list.
an integer vector of length n,
partitioning the data. If the method produces a partition, it
should be the clustering. This component is only used for plots,
so you could do something like rep(1,n) for
non-partitioning methods. If a noise cluster is included,
nc=nccl+1 and the noise cluster is cluster no. nc.
a string indicating the clustering method.
The output of some of the functions has further components:
see nc above.
by noisemclustCBI and distnoisemclustCBI,
see above.
logical vector, indicating initially estimated noise by
NNclean, called by noisemclustCBI
and distnoisemclustCBI.
logical. TRUE if points were classified as
noise/outliers by disthclustCBI.
a numeric matrix. The data
matrix - usually a cases*variables-data matrix. claraCBI,
pamkCBI and dbscanCBI work with an
n*n-dissimilarity matrix as well, see parameter diss.
a squared numerical dissimilarity matrix or a
dist-object.
numeric, usually integer. In most cases, this is the number
of clusters for methods where this is fixed. For hclustCBI
and disthclustCBI see parameter cut below. Some
methods have a k parameter on top of a G or
krange parameter for compatibility; k in these cases
does not have to be specified but if it is, it is always a single
number of clusters and overwrites G and
krange.
either a logical value or a numeric vector of length
equal to the number of variables. If scaling is a numeric
vector with length equal to the number of variables, then each
variable is divided by the corresponding value from scaling.
If scaling is TRUE then scaling is done by dividing
the (centered) variables by their root-mean-square, and if
scaling is FALSE, no scaling is done before execution.
integer. Number of random initializations from which the k-means algorithm is started.
"ch" or "asw". Decides whether number
of clusters is estimated by the Calinski-Harabasz criterion or by the
average silhouette width.
either "level" or "number". This determines how
cutree is used to obtain a partition from a hierarchy
tree. cut="level" means that the tree is cut at a particular
dissimilarity level, cut="number" means that the tree is cut
in order to obtain a fixed number of clusters. The parameter
k specifies the number of clusters or the dissimilarity
level, depending on cut.
method for hierarchical clustering, see the
documentation of hclust.
numeric. All clusters of size <=noisecut in the
disthclustCBI/hclustCBI-partition are joined and declared as
noise/outliers.
integer. minlevel=1 means that all clusters in
the tree are given out by hclusttreeCBI or
disthclusttreeCBI, including one-point
clusters (but excluding the cluster with all
points). minlevel=2 excludes the one-point clusters.
minlevel=3 excludes the two-point cluster which has been
merged first, and increasing the value of minlevel by 1 in
all further steps means that the remaining earliest formed cluster
is excluded.
vector of integers. Number of clusters or numbers of clusters
used by
mclustBIC. If
G has more than one entry, the number of clusters is
estimated by the BIC.
vector of string. Models for covariance matrices,
see documentation of
mclustBIC.
integer. Tuning constant for
NNclean, which is used to estimate the
initial noise for noisemclustCBI and
distnoisemclustCBI. See parameter k in the
documentation of NNclean. nnk=0 means
that no noise component is fitted.
string or NULL. Determines the initialization of
the EM-algorithm for
mclustBIC.
Documented in hc.
numeric. See documentation of
mclustBIC.
logical. If TRUE, the result of
summary.mclustBIC is added as component
mclustsummary to the output of noisemclustCBI and
distnoisemclustCBI.
"classical", "kruskal" or "sammon". Determines the
multidimensional scaling method to compute Euclidean data from a
dissimilarity matrix. See cmdscale,
isoMDS and sammon.
integer. Dimensionality of MDS solution.
logical. If TRUE, the matrix of MDS points
is added as component
points to the output of noisemclustCBI.
logical. If TRUE, the function
pam is used for clustering, otherwise
clara. pam is better,
clara is faster.
logical. If TRUE, data will be considered as
a dissimilarity matrix. In claraCBI, this requires
usepam=TRUE.
vector of integers. Numbers of clusters to be compared.
numeric between 0 and 1. Proportion of data points
trimmed, i.e., assigned to noise. See tclust in the tclust package.
numeric. The radius of the neighborhoods to be considered
by dbscan.
integer. How many points have to be in a neighborhood so
that a point is considered to be a cluster seed? See documentation
of dbscan.
numeric between 0 and 1. If fixmahal
is used for fuzzy clustering, a crisp partition is generated and
points with cluster membership values above clustercut are
considered as members of the corresponding cluster.
method for merging Gaussians, passed on as
method to mergenormals.
numeric between 0 and 1, tuning constant for
mergenormals.
logical (only for stupidkcentroidsCBI). If
FALSE, dmatrix is
interpreted as cases&variables data matrix.
further parameters to be transferred to the original clustering functions (not required).
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/
All these functions call clustering methods implemented in R to
cluster data and to provide output in the format required by
clusterboot. Here is a brief overview. For further
details see the help pages of the involved clustering methods.
an interface to the function
kmeansruns calling kmeans
for k-means clustering. (kmeansruns allows the
specification of several random initializations of the
k-means algorithm and estimation of k by the Calinski-Harabasz
index or the average silhouette width.)
an interface to the function
hclust for agglomerative hierarchical clustering with
noise component (see parameter noisecut above). This
function produces a partition and assumes a cases*variables
matrix as input.
an interface to the function
hclust for agglomerative hierarchical clustering. This
function gives out all clusters belonging to the hierarchy
(upward from a certain level, see parameter minlevel
above).
an interface to the function
hclust for agglomerative hierarchical clustering with
noise component (see parameter noisecut above). This
function produces a partition and assumes a dissimilarity
matrix as input.
% \item{disthclusttreeCBI}{an interface to the function % \code{hclust} for agglomerative hierarchical clustering. This % function gives out all clusters belonging to the hierarchy % (upward from a certain level, see parameter \code{minlevel} % above), and assumes a dissimilarity matrix as input.}
an interface to the function
mclustBIC, for normal mixture model based
clustering. Warning: mclustBIC often
has problems with multiple
points. In clusterboot, it is recommended to use
this together with multipleboot=FALSE.
an interface to the function
mclustBIC for normal mixture model based
clustering. This assumes a dissimilarity matrix as input and
generates a data matrix by multidimensional scaling first.
Warning: mclustBIC often has
problems with multiple
points. In clusterboot, it is recommended to use
this together with multipleboot=FALSE.
an interface to the functions
pam and clara
for partitioning around medoids.
an interface to the function
pamk calling pam for
partitioning around medoids. The number
of clusters is estimated by the Calinski-Harabasz index or by the
average silhouette width.
an interface to the function
tclust in the tclust package for trimmed Gaussian
clustering. This assumes a cases*variables matrix as input.
% % NOTE: This package is currently only available in CRAN as % archived version. Therefore I cannot currently offer the % \code{tclustCBI}-function in \code{fpc}. The code for the % function is below in the Examples-Section, so if you need it, % run that code first.} % \item{disttrimkmeansCBI}{an interface to the function % \code{\link[trimcluster]{trimkmeans}} for trimmed k-means % clustering. This assumes a dissimilarity matrix as input and % generates a data matrix by multidimensional scaling first.}
an interface to the function
dbscan for density based
clustering.
an interface to the function
fixmahal for fixed point
clustering. This assumes a cases*variables matrix as input.
an interface to the function
mergenormals for clustering by merging Gaussian
mixture components. Unlike mergenormals, mergenormCBI
includes the computation of the initial Gaussian mixture.
This assumes a cases*variables matrix as input.
an interface to the function
specc for spectral clustering. See
the specc help page for additional tuning
parameters. This assumes a cases*variables matrix as input.
an interface to the function
pdfCluster for density-based clustering. See
the pdfCluster help page for additional tuning
parameters. This assumes a cases*variables matrix as input.
% \item{emskewCBI}{an interface to the function % \code{\link[EMMIXskew]{EmSkew}} for clustering with the % EM-algorithm based on Gaussian, skew Gaussian, t or skew-t % mixtures. See % help page of \code{\link[EMMIXskew]{EmSkew}}. This assumes a % cases*variables matrix as input. Note that by September 2020, % package \code{EMMIXskew} is not available on CRAN but only % in the CRAN archives; CRAN states that it needs an update.}
an interface to the function
stupidkcentroids for random centroid-based clustering. See
the stupidkcentroids help page. This can have a
distance matrix as well as a cases*variables matrix as input, see
parameter distances.
an interface to the function
stupidknn for random nearest neighbour clustering. See
the stupidknn help page. This assumes a
distance matrix as input.
an interface to the function
stupidkfn for random farthest neighbour clustering. See
the stupidkfn help page. This assumes a
distance matrix as input.
an interface to the function
stupidkaven for random average dissimilarity clustering. See
the stupidkaven help page. This assumes a
distance matrix as input.
options(digits=3)
set.seed(20000)
face <- rFace(50,dMoNo=2,dNoEy=0,p=2)
dbs <- dbscanCBI(face,eps=1.5,MinPts=4)
dhc <- disthclustCBI(dist(face),method="average",k=1.5,noisecut=2)
table(dbs$partition,dhc$partition)
dm <- mergenormCBI(face,G=10,modelNames="EEE",nnk=2)
dtc <- tclustCBI(face,6,trim=0.1,restr.fact=500)
table(dm$partition,dtc$partition)
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