R: Clusterwise cluster stability assessment by resampling

clusterboot {fpc}

R Documentation

Clusterwise cluster stability assessment by resampling

Description

Assessment of the clusterwise stability of a clustering of data, which can be cases*variables or dissimilarity data. The data is resampled using several schemes (bootstrap, subsetting, jittering, replacement of points by noise) and the Jaccard similarities of the original clusters to the most similar clusters in the resampled data are computed. The mean over these similarities is used as an index of the stability of a cluster (other statistics can be computed as well). The methods are described in Hennig (2007).

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.

Usage

clusterboot(data,B=100, distances=(inherits(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,datatomatrix=TRUE,...)

## 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),...)

Arguments

`data`	by default something that can be coerced into a (numerical) matrix (data frames with non-numerical data are allowed when using `datatomatrix=FALSE`, see below). The data matrix - either an `np`-data matrix (or data frame) or an `nn`-dissimilarity matrix (or `dist`-object).
`B`	integer. Number of resampling runs for each scheme, see `bootmethod`.
`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.
`bootmethod`	vector of strings, defining the methods used for resampling. Possible methods: `"boot"`: nonparametric bootstrap (precise behaviour is controlled by parameters `bscompare` and `multipleboot`). `"subset"`: selecting random subsets from the dataset. Size determined by `subtuning`. `"noise"`: replacing a certain percentage of the points by random noise, see `noisetuning`. `"jitter"` add random noise to all points, see `jittertuning`. (This didn't perform well in Hennig (2007), but you may want to get your own experience.) `"bojit"` nonparametric bootstrap first, and then adding noise to the points, see `jittertuning`. Important: only the methods `"boot"` and `"subset"` work with dissimilarity data, or if `datatomatrix=FALSE`! The results in Hennig (2007) indicate that `"boot"` is generally informative and often quite similar to `"subset"` and `"bojit"`, while `"noise"` sometimes provides different information. Therefore the default (for `distances=FALSE`) is to use `"boot"` and `"noise"`. However, some clustering methods may have problems with multiple points, which can be solved by using `"bojit"` or `"subset"` instead of `"boot"` or by `multipleboot=FALSE` below.
`bscompare`	logical. If `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 original cluster which also occur in the bootstrap sample). If a point was drawn more than once, it is in the "bootstrap version" of the original cluster more than once, too, if `bscompare=TRUE`. Otherwise multiple points are ignored for the computation of the Jaccard similarities. If `multipleboot=FALSE`, it doesn't make a difference.
`multipleboot`	logical. If `FALSE`, all points drawn more than once in the bootstrap draw are only used once in the bootstrap samples.
`jittertuning`	positive numeric. Tuning for the `"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 deviation along the principal directions is determined by the `jittertuning`-quantile of the distances between neighboring points projected along these directions.
`noisetuning`	A vector of two positive numerics. Tuning for the `"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 principal directions of the original data set, ranging from `-noisetuning[2]` to `noisetuning[2]` times the standard deviation of the data set along the respective direction. Note that only points not replaced by noise are considered for the computation of Jaccard similarities.
`subtuning`	integer. Size of subsets for `"subset"`.
`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 for a list of available interface functions and guidelines how to write your own ones.
`noisemethod`	logical. If `TRUE`, the last cluster is regarded as "noise cluster", which means that for computing the Jaccard similarity, it is not treated as a cluster. The noise cluster of the original clustering is only compared with the noise cluster of the clustering of the resampled data. This means that in the `clusterboot`-output (and plot), if points were assigned to the noise cluster, the last cluster number refers to it, and its Jaccard similarity values refer to comparisons with estimated noise components in resampled datasets only. (Some cluster methods such as `tclust` and `mclustBIC` produce such noise components.)
`count`	logical. If `TRUE`, the resampling runs are counted on the screen.
`showplots`	logical. If `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. Ignored if `datatomatrix=FALSE`.
`dissolution`	numeric between 0 and 1. If the Jaccard similarity between the resampling version of the original cluster and the most similar cluster on the resampled data is smaller or equal to this value, the cluster is considered as "dissolved". Numbers of dissolved clusters are recorded.
`recover`	numeric between 0 and 1. If the Jaccard similarity between the resampling version of the original cluster and the most similar cluster on the resampled data is larger than this value, the cluster is considered as "successfully recovered". Numbers of recovered clusters are recorded.
`seed`	integer. Seed for random generator (fed into `set.seed`) to make results reproducible. If `NULL`, results depend on chance.
`datatomatrix`	logical. If `TRUE`, `data` is coerced into a (numerical) matrix at the start of `clusterboot`. `FALSE` may be chosen for mixed type data including e.g. categorical factors (assuming that the chosen `clustermethod` allows for this). This disables some features of `clusterboot`, see parameters `bootmethod` and `showplots`.
`...`	additional parameters for the clustermethods called by `clusterboot`. No effect in `print.clboot` and `plot.clboot`.
`x`	object of class `clboot`.
`statistics`	specifies in `print.clboot`, which of the three clusterwise Jaccard similarity statistics `"mean"`, `"dissolution"` (number of times the cluster has been dissolved) and `"recovery"` (number of times a cluster has been successfully recovered) is printed.
`xlim`	transferred to `hist`.
`breaks`	transferred to `hist`.

Details

Here are some guidelines for interpretation. There is some theoretical justification to consider a Jaccard similarity value smaller or equal to 0.5 as an indication of a "dissolved cluster", see Hennig (2008). Generally, a valid, stable cluster should yield a mean Jaccard similarity value of 0.75 or more. Between 0.6 and 0.75, clusters may be considered as indicating patterns in the data, but which points exactly should belong to these clusters is highly doubtful. Below average Jaccard values of 0.6, clusters should not be trusted. "Highly stable" clusters should yield average Jaccard similarities of 0.85 and above. All of this refers to bootstrap; for the other resampling schemes it depends on the tuning constants, though their default values should grant similar interpretations in most cases.

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:

kmeansCBI: an interface to the function kmeans for k-means clustering. This assumes a cases*variables matrix as input.
hclustCBI: an interface to the function hclust for agglomerative hierarchical clustering with optional noise cluster. This function produces a partition and assumes a cases*variables matrix as input.
hclusttreeCBI: an interface to the function hclust for agglomerative hierarchical clustering. This function produces a tree (not only a partition; therefore the number of clusters can be huge!) and assumes a cases*variables matrix as input.
disthclustCBI: an interface to the function hclust for agglomerative hierarchical clustering with optional noise cluster. This function produces a partition and assumes a dissimilarity matrix as input.
noisemclustCBI: an interface to the function mclustBIC for normal mixture model based clustering. This assumes a cases*variables matrix as input. Warning: mclustBIC sometimes has problems with multiple points. It is recommended to use this only together with multipleboot=FALSE.
distnoisemclustCBI: 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 sometimes has problems with multiple points. It is recommended to use this only together with multipleboot=FALSE.
claraCBI: an interface to the functions pam and clara for partitioning around medoids. This can be used with cases*variables as well as dissimilarity matrices as input.
pamkCBI: an interface to the function pamk for partitioning around medoids. The number of cluster is estimated by the average silhouette width. This can be used with cases*variables as well as dissimilarity matrices as input.
tclustCBI: an interface to the function tclust in the tclust library for trimmed Gaussian clustering. This assumes a cases*variables matrix as input. Note that this function is not currently provided because the tclust package is only available in the CRAN archives, but the code is in the Examples-section of the kmeansCBI-help page.
dbscanCBI: an interface to the function dbscan for density based clustering. This can be used with cases*variables as well as dissimilarity matrices as input..
mahalCBI: an interface to the function fixmahal for fixed point clustering. This assumes a cases*variables matrix as input.
mergenormCBI: an interface to the function mergenormals for clustering by merging Gaussian mixture components.
speccCBI: an interface to the function specc for spectral clustering.

You can write your own interface function. The first argument of an interface function should preferably be a data matrix (of class "matrix", but it may be a symmetrical dissimilarity matrix). It can be a data frame, but this restricts some of the functionality of clusterboot, see above. 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:

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 cluster, and there should be another component nccl, being the number of clusters not including the noise cluster (note that it is not mandatory to define a noise component if not all points are assigned to clusters, but if you do it, the stability of the noise cluster is assessed as well.)
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 cluster 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. If a noise cluster is included, nc=nccl+1 and the noise cluster is cluster no. nc.
clustermethod: a string indicating the clustering method.

Value

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.

`result`	clustering result; full output of the selected `clustermethod` for the original data set.
`partition`	partition parameter of the selected `clustermethod` (note that this is only meaningful for partitioning clustering methods).
`nc`	number of clusters in original data (including noise component if `noisemethod=TRUE`).
`nccl`	number of clusters in original data (not including noise component if `noisemethod=TRUE`).
`clustermethod`, `B`, `noisemethod`, `bootmethod`, `multipleboot`, `dissolution`, `recover`	input parameters, see above.
`bootresult`	matrix of Jaccard similarities for `bootmethod="boot"`. Rows correspond to clusters in the original data set. Columns correspond to bootstrap runs.
`bootmean`	clusterwise means of the `bootresult`.
`bootbrd`	clusterwise number of times a cluster has been dissolved.
`bootrecover`	clusterwise number of times a cluster has been successfully recovered.
`subsetresult`, `subsetmean`, `etc.`	same as `bootresult, bootmean, etc.`, but for the other resampling methods.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/

References

Hennig, C. (2007) Cluster-wise assessment of cluster stability. Computational Statistics and Data Analysis, 52, 258-271.

Hennig, C. (2008) Dissolution point and isolation robustness: robustness criteria for general cluster analysis methods. Journal of Multivariate Analysis 99, 1154-1176.

Examples

  options(digits=3)
  set.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)
  d1 <- c("a","b","a","c")
  d2 <- c("a","a","a","b")
  dx <- as.data.frame(cbind(d1,d2))
  cpx <- clusterboot(dx,k=2,B=10,clustermethod=claraCBI,
          multipleboot=TRUE,usepam=TRUE,datatomatrix=FALSE)
  print(cpx)

[Package fpc version 2.2-12 Index]