fpc (version 2.2-9)

stupidkcentroids: Stupid k-centroids random clustering

Description

Picks k random centroids from given dataset and assigns every point to closest centroid. This is called stupid k-centroids in Hennig (2019).

Usage

stupidkcentroids(xdata, k, distances = inherits(xdata, "dist"))

Arguments

xdata

cases*variables data, dist-object or dissimilarity matrix, see distances.

k

integer. Number of clusters.

distances

logical. If TRUE, xdata is interpreted as distances.

Value

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

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

stupidknn, stupidkfn, stupidkaven

Examples

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