kmeansCBI {fpc}R Documentation

Interface functions for clustering methods

Description

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).

Usage

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,...)

Arguments

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).

Details

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:

Value

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.

Author(s)

Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/

See Also

clusterboot, dist, kmeans, kmeansruns, hclust, EMclust, EMclustN, pam, pamk, clara, trimkmeans, dbscan, fixmahal

Examples

  
  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)

[Package fpc version 1.2-3 Index]