fpc (version 2.2-9)

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 (2019).

Usage

stupidknn(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

Hennig, C. (2019) Cluster validation by measurement of clustering characteristics relevant to the user. In C. H. Skiadas (ed.) Data Analysis and Applications 1: Clustering and Regression, Modeling-estimating, Forecasting and Data Mining, Volume 2, Wiley, New York 1-24, https://arxiv.org/abs/1703.09282

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, stupidkfn, stupidkaven

Examples

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