kmeansCBI | R Documentation |
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)
data |
a numeric matrix. The data
matrix - usually a cases*variables-data matrix. |
dmatrix |
a squared numerical dissimilarity matrix or a
|
k |
numeric, usually integer. In most cases, this is the number
of clusters for methods where this is fixed. For |
scaling |
either a logical value or a numeric vector of length
equal to the number of variables. If |
runs |
integer. Number of random initializations from which the k-means algorithm is started. |
criterion |
|
cut |
either "level" or "number". This determines how
|
method |
method for hierarchical clustering, see the
documentation of |
noisecut |
numeric. All clusters of size |
minlevel |
integer. |
G |
vector of integers. Number of clusters or numbers of clusters
used by
|
modelNames |
vector of string. Models for covariance matrices,
see documentation of
|
nnk |
integer. Tuning constant for
|
hcmodel |
string or |
Vinv |
numeric. See documentation of
|
summary.out |
logical. If |
mdsmethod |
"classical", "kruskal" or "sammon". Determines the
multidimensional scaling method to compute Euclidean data from a
dissimilarity matrix. See |
mdsdim |
integer. Dimensionality of MDS solution. |
points.out |
logical. If |
usepam |
logical. If |
diss |
logical. If |
krange |
vector of integers. Numbers of clusters to be compared. |
trim |
numeric between 0 and 1. Proportion of data points
trimmed, i.e., assigned to noise. See |
eps |
numeric. The radius of the neighborhoods to be considered
by |
MinPts |
integer. How many points have to be in a neighborhood so
that a point is considered to be a cluster seed? See documentation
of |
clustercut |
numeric between 0 and 1. If |
mergemethod |
method for merging Gaussians, passed on as
|
cutoff |
numeric between 0 and 1, tuning constant for
|
distances |
logical (only for |
... |
further parameters to be transferred to the original clustering functions (not required). |
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.
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.
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.
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.
All interface functions return a list with the following components
(there may be some more, see summary.out
and points.out
above):
result |
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. |
nc |
number of clusters. If some points don't belong to any
cluster, these are declared "noise". |
clusterlist |
this is a list consisting of a logical vectors
of length of the number of data points ( |
partition |
an integer vector of length |
clustermethod |
a string indicating the clustering method. |
The output of some of the functions has further components:
nccl |
see |
nnk |
by |
initnoise |
logical vector, indicating initially estimated noise by
|
noise |
logical. |
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/
clusterboot
, dist
,
kmeans
, kmeansruns
, hclust
,
mclustBIC
,
pam
, pamk
,
clara
,
dbscan
,
fixmahal
,
tclust
, pdfCluster
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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