kclustering: K-means cluster analysis

In BasketballAnalyzeR: Analysis and Visualization of Basketball Data

K-means cluster analysis

Description

K-means cluster analysis

Usage

kclustering(
  data,
  k = NULL,
  labels = NULL,
  nclumax = 10,
  nruns = 10,
  iter.max = 50,
  algorithm = "Hartigan-Wong"
)

Arguments

data	numeric data frame.
k	integer, number of clusters.
labels	character, row labels.
nclumax	integer, maximum number of clusters (when k=NULL) used for calculating the explained variance as function of the number of clusters.
nruns	integer, run the k-means algorithm nruns times and chooses the best solution according to a maximum explained variance criterion.
iter.max	integer, maximum number of iterations allowed in k-means clustering (see kmeans).
algorithm	character, the algorithm used in k-means clustering (see kmeans).

Details

The kclustering function performs a preliminary standardization of columns in data.

Value

A kclustering object.

If k is NULL, the kclustering object is a list of 3 elements:

• k NULL

• clusterRange integer vector, values of k (from 1 to nclumax) at which the variance between of the clusterization is evaluated

• VarianceBetween numeric vector, values of the variance between evaluated for k in clusterRange

If k is not NULL, the kclustering object is a list of 4 elements:

• k integer, number of clusters

• Subjects data frame, subjects' cluster identifiers

• ClusterList list, clusters' composition

• Profiles data frame, clusters' profiles, i.e. the average of the variables within clusters and the cluster eterogeineity index (CHI)

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([e-mail])

References

P. Zuccolotto and M. Manisera (2020) Basketball Data Science: With Applications in R. CRC Press.

See Also

plot.kclustering, kmeans

Examples

FF <- fourfactors(Tbox,Obox)
X <- with(FF, data.frame(OD.Rtg=ORtg/DRtg,
               F1.r=F1.Def/F1.Off, F2.r=F2.Off/F2.Def,
               F3.O=F3.Def, F3.D=F3.Off))
X$P3M <- Tbox$P3M
X$STL.r <- Tbox$STL/Obox$STL
kclu1 <- kclustering(X)
plot(kclu1)
kclu2 <- kclustering(X, k=9)
plot(kclu2)

BasketballAnalyzeR documentation built on June 8, 2025, 10:12 a.m.