fpc (version 2.2-13)

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)

Value

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

Arguments

d

dist-object or dissimilarity matrix.

k

integer. Number of clusters.

Author

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

References

Akhanli, S. and Hennig, C. (2020) Calibrating and aggregating cluster validity indexes for context-adapted comparison of clusterings. Statistics and Computing, 30, 1523-1544, https://link.springer.com/article/10.1007/s11222-020-09958-2, https://arxiv.org/abs/2002.01822

See Also

stupidkcentroids, stupidknn, stupidkaven

Examples

  set.seed(20000)
  options(digits=3)
  face <- rFace(200,dMoNo=2,dNoEy=0,p=2)
  stupidkfn(dist(face),3)