clusterboot is an integrated function that computes the
clustering as well, using interface functions for various
clustering methods implemented in R (several interface functions are
provided, but you can
implement further ones for your favourite clustering method). See the
documentation of the input parameter clustermethod below.
Quite general clustering methods are possible, i.e. methods estimating or fixing the number of clusters, methods producing overlapping clusters or not assigning all cases to clusters (but declaring them as "noise"). Fuzzy clusterings cannot be processed and have to be transformed to crisp clusterings by the interface function.
clusterboot(data,B=100, distances=(class(data)=="dist"),
bootmethod="boot",
bscompare=TRUE,
multipleboot=FALSE,
jittertuning=0.05, noisetuning=c(0.05,4),
subtuning=floor(nrow(data)/2),
clustermethod,noisemethod=FALSE,count=TRUE,
showplots=FALSE,dissolution=0.5,
recover=0.75,seed=NULL,...)## S3 method for class 'clboot':
print(x,statistics=c("mean","dissolution","recovery"),...)
## S3 method for class 'clboot':
plot(x,xlim=c(0,1),breaks=seq(0,1,by=0.05),...)
n*p-data matrix (or data frame) or an
n*n-dissimilarity matrix (or dist-object).bootmethod.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 y"boot": nonparametric bootstrap (precise behaviour is
controlled by parameters bscompare and
multipleboot).
TRUE, multiple points in the
bootstrap sample are taken into account to compute the Jaccard
similarity to the original clusters (which are represented by their
"bootstrap versions", i.e., the
points of the originalFALSE, all points drawn more
than once in the bootstrap draw are only used once in the bootstrap
samples."jitter"-method. The noise distribution for
jittering is a normal distribution with zero mean. The covariance
matrix has the same Eigenvectors as that of the original
data set, but the standard"noise"-method. The first component determines the
probability that a point is replaced by noise. Noise is generated by
a uniform distribution on a hyperrectangle along the princip"subset".TRUE, the last cluster is
regarded as "noise component", which means that for computing the Jaccard
similarity, it is not treated as a cluster. The noise component of
the original clustering is only compared with the nTRUE, the resampling runs are counted
on the screen.TRUE, a plot of the first two
dimensions of the resampled data set (or the classical MDS solution
for dissimilarity data) is shown for every resampling run. The last
plot shows the original data set.set.seed) to make results reproducible. If NULL,
results depend on chance.clusterboot. No effect in print.clboot and
plot.clboot.clboot.print.clboot,
which of the three clusterwise Jaccard
similarity statistics "mean", "dissolution" (number of
times the cluster has been dissolved) and "recovery" (number
of tihist.hist.clusterboot returns an object of class "clboot", which
is a list with components
result, partition, nc, clustermethod, B, noisemethod, bootmethod,
multipleboot, dissolution, recover, bootresult, bootmean, bootbrd,
bootrecover, jitterresult, jittermean, jitterbrd, jitterrecover,
subsetresult, subsetmean, subsetbrd, subsetrecover, bojitresult,
bojitmean, bojitbrd, bojitrecover, noiseresult, noisemean,
noisebrd, noiserecover.clustermethod for the original data set.clustermethod
(note that this is only meaningful for partitioning clustering methods).noisemethod=TRUE).noisemethod=TRUE).bootmethod="boot". Rows correspond to clusters in the
original data set. Columns correspond to bootstrap runs.bootresult.bootresult,
bootmean, etc., but for the other resampling methods. While B=100 is recommended, smaller run numbers could give
quite informative results as well, if computation times become too high.
Note that the stability of a cluster is assessed, but
stability is not the only important validity criterion - clusters
obtained by very inflexible clustering methods may be stable but not
valid, as discussed in Hennig (2007).
See plotcluster for graphical cluster validation.
Information about interface functions for clustering methods:
The following interface functions are currently
implemented (in the present package; note that almost all of these
functions require the specification of some control parameters, so
if you use one of them, look up their common help page
kmeansCBI) first:
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
You can write your own interface function. The first argument of an
interface function should always be a data matrix (of class
"matrix", but it may be a symmetrical dissimilarity matrix). Further
arguments can be tuning constants for the clustering method. The
output of an interface function should be a list containing (at
least) the following components:
[object Object],[object Object],[object Object],[object Object],[object Object]
Hennig, C. (2008) Dissolution point and isolation robustness: robustness criteria for general cluster analysis methods. Journal of Multivariate Analysis 99, 1154-1176.
dist,
interface functions:
kmeansCBI, hclustCBI,
hclusttreeCBI, disthclustCBI,
noisemclustCBI, distnoisemclustCBI,
claraCBI, pamkCBI,
trimkmeansCBI, disttrimkmeansCBI,
dbscanCBI, mahalCBIset.seed(20000)
face <- rFace(50,dMoNo=2,dNoEy=0,p=2)
cf1 <- clusterboot(face,B=3,bootmethod=
c("boot","noise","jitter"),clustermethod=kmeansCBI,
krange=5,seed=15555) print(cf1)
plot(cf1) cf2 <- clusterboot(dist(face),B=3,bootmethod=
"subset",clustermethod=disthclustCBI,
k=5, cut="number", method="average", showplots=TRUE, seed=15555)
print(cf2)Run the code above in your browser using DataLab