kmeansCBI {fpc} | 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,k,scaling=TRUE,runs=1,...) hclustCBI(data,k,cut="level",method,scaling=TRUE,noisecut=0,...) hclusttreeCBI(data,minlevel=2,method,scaling=TRUE,...) disthclustCBI(dmatrix,k,cut="level",method,noisecut=0,...) noisemclustCBI(data,G,emModelNames,nnk,hcmodel=NULL,Vinv=NULL) distnoisemclustCBI(dmatrix,G,emModelNames,nnk, hcmodel=NULL,Vinv=NULL,mdsmethod="classical", mdsdim=4) claraCBI(data,k,usepam=TRUE,diss=FALSE,...) pamkCBI(data,krange=2:10,scaling=TRUE,diss=FALSE,...) trimkmeansCBI(data,k,scaling=TRUE,trim=0.1,...) disttrimkmeansCBI(dmatrix,k,scaling=TRUE,trim=0.1, mdsmethod="classical", mdsdim=4,...) dbscanCBI(data,eps,MinPts,diss=FALSE,...) mahalCBI(data,clustercut=0.5,...)
data |
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 . |
dmatrix |
a squared numerical dissimilarity matrix or a
dist -object. |
k |
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. |
scaling |
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. |
runs |
integer. Number of random initializations from which the k-means algorithm is started. |
cut |
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 |
method for hierarchical clustering, see the
documentation of hclust . |
noisecut |
numeric. All clusters of size <=noisecut in the
disthclustCBI /hclustCBI -partition are joined and declared as
noise/outliers. |
minlevel |
integer. minlevel=1 means that all clusters in
the tree are given out by hclusttreeCBI , 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. |
G |
vector of integers. Number of clusters or numbers of clusters
used by
EMclust /EMclustN . If
G has more than one entry, the number of clusters is
estimated by the BIC. |
emModelNames |
vector of string. Models for covariance matrices,
see documentation of
EMclust /EMclustN . |
nnk |
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. |
hcmodel |
string or NULL . Determines the initialization of
the EM-algorithm for
EMclust /EMclustN .
Documented in hc . |
Vinv |
numeric. See documentation of
EMclustN . |
mdsmethod |
"classical", "kruskal" or "sammon". Determines the
multidimensional scaling method to compute Euclidean data from a
dissimilarity matrix. See cmdscale ,
isoMDS and sammon . |
mdsdim |
integer. Dimensionality of MDS solution. |
usepam |
logical. If TRUE , the function
pam is used for clustering, otherwise
clara . pam is better,
clara is faster. |
diss |
logical. If TRUE , data will be considered as
a dissimilarity matrix. in claraCBI , this requires
usepam=TRUE . |
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
trimkmeans . |
eps |
numeric. The radius of the neighborhoods to be considered
by dbscan . |
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 dbscan . |
clustercut |
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. |
... |
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:
kmeansruns
calling kmeans
for k-means clustering. (kmeansruns
allows the
specification of several random initializations of the
k-means algorithm.)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.hclust
for agglomerative hierarchical clustering. This
function gives out all clusters belonging to the hierarchy
(upward from a certain level, see parameter minlevel
above).hclust
for agglomerative hierarchical clustering with
noise component (see parameter noisecut
above). This
function produces a partition and assumes a dissimilarity
matrix as input.EMclust
and
EMclustN
, for normal mixture model based
clustering. Warning: EMclust
and
EMclustN
often have problems with multiple
points. In clusterboot
, it is recommended to use
this only together with multipleboot=FALSE
.
NOTE: the mclust package has recently been updated to 3.0.0.
noisemclustCBI
at the moment
requires one of the previous versions of mclust. The newest one is now
available as mclust02 on CRAN. If you have an older version of
mclust installed, which is still named "mclust", you have to
change require(mclust02)
in the function to
require(mclust)
.
EMclust
and
EMclustN
, for normal mixture model based
clustering. This assumes a dissimilarity matrix as input and
generates a data matrix by multidimensional scaling first.
Warning: EMclust
and
EMclustN
often have problems with multiple
points. In clusterboot
, it is recommended to use
this only together with multipleboot=FALSE
.
NOTE: the mclust package has recently been updated to 3.0.0.
distnoisemclustCBI
at the moment
requires one of the previous versions of mclust. The newest one is now
available as mclust02 on CRAN. If you have an older version of
mclust installed, which is still named "mclust", you have to
change require(mclust02)
in the function to
require(mclust)
.
pam
and clara
for partitioning around medoids.pamk
calling pam
for
partitioning around medoids. The number
of clusters is estimated by the average silhouette width.trimkmeans
for trimmed k-means
clustering. This assumes a cases*variables matrix as input.trimkmeans
for trimmed k-means
clustering. This assumes a dissimilarity matrix as input and
generates a data matrix by multidimensional scaling first.dbscan
for density based
clustering.fixmahal
for fixed point
clustering.All interface functions return a list with the following components:
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 but are declared as "noise", nc includes the
noise component, and there should be another component
nccl , being the number of clusters not including the
noise component. |
clusterlist |
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 component is included, it
should always be the last vector in this list. |
partition |
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. |
clustermethod |
a string indicating the clustering method. |
nccl |
see nc above. |
nnk |
by noisemclustCBI and distnoisemclustCBI ,
see above. |
initnoise |
logical vector, indicating initially estimated noise by
NNclean , called by noisemclustCBI
and distnoisemclustCBI . |
noise |
logical. TRUE if points were classified as
noise/outliers by disthclustCBI . |
Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/
clusterboot
, dist
,
kmeans
, kmeansruns
, hclust
,
EMclust
, EMclustN
,
pam
, pamk
,
clara
,
trimkmeans
, dbscan
,
fixmahal
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)