fpc (version 2.2-7)

stupidkfn: Stupid farthest neighbour random clustering

Description

Picks k random starting points from given dataset to initialise k clusters. Then, one by one, a point not yet assigned to any cluster is assigned to that cluster, until all points are assigned. The point/cluster pair to be used is picked according to the smallest distance of a point to the farthest point to it in any of the already existing clusters as in complete linkage clustering, see Akhanli and Hennig (2020).

Usage

stupidkfn(d,k)

Arguments

d

dist-object or dissimilarity matrix.

k

integer. Number of clusters.

Value

The clustering vector (values 1 to k, length number of objects behind d),

References

Akhanli, S. and Hennig, C. (2020) Calibrating and aggregating cluster validity indexes for context-adapted comparison of clusterings. Accepted for publication by Statistics and Computing, https://arxiv.org/abs/2002.01822

See Also

stupidkcentroids, stupidknn, stupidkaven

Examples

# NOT RUN {
  set.seed(20000)
  options(digits=3)
  face <- rFace(200,dMoNo=2,dNoEy=0,p=2)
  stupidkfn(dist(face),3) 
# }