nselectboot: Selection of the number of clusters via bootstrap

Description

Selection of the number of clusters via bootstrap as explained in Fang and Wang (2012). Several times 2 bootstrap samples are drawn from the data and the number of clusters is chosen by optimising an instability estimation from these pairs.

In principle all clustering methods can be used that have a CBI-wrapper, see clusterboot, kmeansCBI. However, the currently implemented classification methods are not necessarily suitable for all of them, see argument classification.

Usage

nselectboot(data,B=50,distances=inherits(data,"dist"),
                        clustermethod=NULL,
                        classification="averagedist",krange=2:10,
                        count=FALSE,nnk=1, ...)

Arguments

data

something that can be coerced into a matrix. The data matrix - either an n*p-data matrix (or data frame) or an n*n-dissimilarity matrix (or dist-object).

integer. Number of resampling runs.

distances

logical. If TRUE, the data is interpreted as dissimilarity matrix. If data is a dist-object, distances=TRUE automatically, otherwise distances=FALSE by default. This means that you have to set it to TRUE manually if data is a dissimilarity matrix.

clustermethod

an interface function (the function name, not a string containing the name, has to be provided!). This defines the clustering method. See the "Details"-section of clusterboot and kmeansCBI for the format. Clustering methods for nselectboot must have a k-argument for the number of clusters and must otherwise follow the specifications in clusterboot. Note that nselectboot won't work with CBI-functions that implicitly already estimate the number of clusters such as pamkCBI; use claraCBI if you want to run it for pam/clara clustering.

classification

string. This determines how non-clustered points are classified to given clusters. Options are explained in classifdist (if distances=TRUE) and classifnp (otherwise). Certain classification methods are connected to certain clustering methods. classification="averagedist" is recommended for average linkage, classification="centroid" is recommended for k-means, clara and pam (with distances it will work with claraCBI only), classification="knn" with nnk=1 is recommended for single linkage and classification="qda" is recommended for Gaussian mixtures with flexible covariance matrices.

krange

integer vector; numbers of clusters to be tried.

count

logical. If TRUE, numbers of clusters and bootstrap runs are printed.

nnk

number of nearest neighbours if classification="knn", see classifdist (if distances=TRUE) and classifnp (otherwise).

...

arguments to be passed on to the clustering method.

Value

nselectboot returns a list with components kopt,stabk,stab.

kopt

optimal number of clusters.

stabk

mean instability values for numbers of clusters.

stab

matrix of instability values for all bootstrap runs and numbers of clusters.

References

Fang, Y. and Wang, J. (2012) Selection of the number of clusters via the bootstrap method. Computational Statistics and Data Analysis, 56, 468-477.

Examples

Run this code

# NOT RUN {
  set.seed(20000)
  face <- rFace(50,dMoNo=2,dNoEy=0,p=2)
  nselectboot(dist(face),B=2,clustermethod=disthclustCBI,
   method="average",krange=5:7)
  nselectboot(dist(face),B=2,clustermethod=claraCBI,
   classification="centroid",krange=5:7)
  nselectboot(face,B=2,clustermethod=kmeansCBI,
   classification="centroid",krange=5:7)
# Of course use larger B in a real application.
# }

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