fpc (version 2.2-4)

stupidknn: Stupid nearest neighbour 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 that is closest to an already assigned point is assigned to that cluster, until all points are assigned. This is called stupid nearest neighbour clustering in Hennig (2017).

Usage

stupidknn(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

Hennig, C. (2017) Cluster validation by measurement of clustering characteristics relevant to the user. In C. H. Skiadas (ed.) Proceedings of ASMDA 2017, 501-520, https://arxiv.org/abs/1703.09282

Akhanli, S. and Hennig, C. (2020) Calibrating and aggregating cluster validity indexes for context-adapted comparison of clusterings. On arxiv from February 2020.

See Also

stupidkcentroids, stupidkfn, stupidkaven

Examples

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

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