stupidkaven: Stupid average dissimilarity random clustering

In fpc: Flexible Procedures for Clustering

Stupid average dissimilarity random clustering

Description

Picks k random starting points from given dataset to initialise k clusters. Then, one by one, the point not yet assigned to any cluster with smallest average dissimilarity to the points of any already existing cluster is assigned to that cluster, until all points are assigned. This is a random versione of average linkage clustering, see Akhanli and Hennig (2020).

Usage

  stupidkaven(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, stupidkfn

Examples

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

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