maximiseSilhouette: Choose K that maximises the silhouette from a set of kernel...
In coca: Cluster-of-Clusters Analysis

Description Usage Arguments Value Author(s)

Choose the number of clusters K that maximises the silhouette, starting from a set of kernel matrices each corresponding to a different choice of K and the corresponding clusterings of the data for each of those values of K.

maximiseSilhouette(
  kernelMatrix,
  clLabels,
  maxK,
  savePNG = FALSE,
  fileName = "silhouette",
  isDistance = FALSE,
  widestGap = FALSE,
  dunns = FALSE,
  dunn2s = FALSE
)

`kernelMatrix`	N X N X (maxK-1) array of kernel matrices.
`clLabels`	(maxK-1) X N matrix containing the clusterings obtained for different values of K.
`maxK`	Maximum number of clusters considered.
`savePNG`	If TRUE, a plot of the silhouette is saved in the working folder. Defaults to FALSE.
`fileName`	If `savePNG` is TRUE, this is the name of the png file.
`isDistance`	Boolean. If TRUE, the kernel matrices are interpreted as matrices of distances, otherwise as matrices of similarities.
`widestGap`	Boolean. If TRUE, also computes widest gap index (and plots it if `savePNG` is TRUE).
`dunns`	Boolean. If TRUE, also computes Dunn's index: minimum separation / maximum diameter (and plots it if `savePNG` is TRUE).
`dunn2s`	Boolean. If TRUE, also computes an alternative version of Dunn's index: minimum average dissimilarity between two cluster / maximum average within cluster dissimilarity (and plots it if `savePNG` is TRUE).

The function returns a list containing:

`silh`	a vector of length `maxK-1` such that `silh[i]` is the silhouette for `K = i+1`
`K`	the lowest number of clusters for which the silhouette is maximised.