Package 'fpc' - open source solution for an Internet free, intelligent ...

30 clusterboot
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 component. 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 component. 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.
trimkmeansCBI an interface to the function trimkmeans for trimmed k-means clustering. This
assumes a cases*variables matrix as input.
tclustCBI an interface to the function tclust for trimmed Gaussian clustering. This assumes a
cases*variables matrix as input.
disttrimkmeansCBI an interface to the function trimkmeans for trimmed k-means clustering.
This assumes a dissimilarity matrix as input and generates a data matrix by multidimensional
scaling first.