fpc: cluster.magazine – R documentation

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cluster.magazine

Run many clustering methods on many numbers of clusters

Description

Runs a user-specified set of clustering methods (CBI-functions, see kmeansCBI with several numbers of clusters on a dataset with unified output.

Usage

cluster.magazine(data,G,diss = inherits(data, "dist"),
                             scaling=TRUE, clustermethod,
                             distmethod=rep(TRUE,length(clustermethod)),
                             ncinput=rep(TRUE,length(clustermethod)),
                             clustermethodpars,
                             trace=TRUE)

Arguments

`data`	data matrix or `dist`-object.
`G`	vector of integers. Numbers of clusters to consider.
`diss`	logical. If `TRUE`, the data matrix is assumed to be a distance/dissimilariy matrix, otherwise it's observations times variables.
`scaling`	either a logical or a numeric vector of length equal to the number of columns of `data`. If `FALSE`, data won't be scaled, otherwise `scaling` is passed on to `scale` as argument`scale`.
`clustermethod`	vector of strings specifying names of CBI-functions (see `kmeansCBI`). These are the clustering methods to be applied.
`distmethod`	vector of logicals, of the same length as `clustermethod`. `TRUE` means that the clustering method operates on distances. If `diss=TRUE`, all entries have to be `TRUE`. Otherwise, if an entry is true, the corresponding method will be applied on `dist(data)`.
`ncinput`	vector of logicals, of the same length as `clustermethod`. `TRUE` indicates that the corresponding clustering method requires the number of clusters as input and will not estimate the number of clusters itself.
`clustermethodpars`	list of the same length as `clustermethod`. Specifies parameters for all involved clustering methods. Its jth entry is passed to clustermethod number k. Can be an empty entry in case all defaults are used for a clustering method. The number of clusters does not need to be specified here.
`trace`	logical. If `TRUE`, some runtime information is printed.

Value

List of lists comprising

`output`	Two-dimensional list. The first list index i is the number of the clustering method (ordering as specified in `clustermethod`), the second list index j is the number of clusters. This stores the full output of clustermethod i run on number of clusters j.
`clustering`	Two-dimensional list. The first list index i is the number of the clustering method (ordering as specified in `clustermethod`), the second list index j is the number of clusters. This stores the clustering integer vector (i.e., the `partition`-component of the CBI-function, see `kmeansCBI`) of clustermethod i run on number of clusters j.
`noise`	Two-dimensional list. The first list index i is the number of the clustering method (ordering as specified in `clustermethod`), the second list index j is the number of clusters. List entries are single logicals. If `TRUE`, the clustering method estimated some noise, i.e., points not belonging to any cluster, which in the clustering vector are indicated by the highest number (number of clusters plus one in case that the number of clusters was fixed).
`othernc`	list of integer vectors of length 2. The first number is the number of the clustering method (the order is determined by argument `clustermethod`), the second number is the number of clusters for those methods that estimate the number of clusters themselves and estimate a number that is smaller than `min(G)` or larger than `max(G)`.

Author(s)

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

References

Hennig, C. (2017) Cluster validation by measurement of clustering characteristics relevant to the user. In C. H. Skiadas (ed.) Proceedings of ASMDA 2017, 501-520, https://arxiv.org/abs/1703.09282

Examples

set.seed(20000)
  options(digits=3)
  face <- rFace(10,dMoNo=2,dNoEy=0,p=2)
  clustermethod=c("kmeansCBI","hclustCBI","hclustCBI")
# A clustering method can be used more than once, with different
# parameters
  clustermethodpars <- list()
  clustermethodpars[[2]] <- clustermethodpars[[3]] <- list()
  clustermethodpars[[2]]$method <- "complete"
  clustermethodpars[[3]]$method <- "average"
  cmf <-  cluster.magazine(face,G=2:3,clustermethod=clustermethod,
    distmethod=rep(FALSE,3),clustermethodpars=clustermethodpars)
  print(str(cmf))

fpc

Flexible Procedures for Clustering

v2.2-9

GPL

Authors

Christian Hennig <christian.hennig@unibo.it>

Initial release

2020-12-06