stupidkcentroids: Stupid k-centroids random clustering

In fpc: Flexible Procedures for Clustering

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

A list with components

partition	vector if integers 1 to k, of length equal to number of objects, indicates to which cluster an object belongs.
centroids	vector of integers of length k, indicating the centroids of the clusters (observation number).
distances	as argument distances.

Author(s)

Christian Hennig [e-mail] 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

stupidknn, stupidkfn, stupidkaven

Examples

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

fpc documentation built on Sept. 24, 2024, 9:07 a.m.