stupidkfn: Stupid farthest neighbour random clustering

In fpc: Flexible Procedures for Clustering

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),

Author(s)

Christian Hennig [e-mail] 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) 

fpc documentation built on Jan. 7, 2023, 1:13 a.m.