hclustering: Agglomerative hierarchical clustering

In sndmrc/BasketAnalyzeR: Analysis and Visualization of Basketball Data

Agglomerative hierarchical clustering

Description

Agglomerative hierarchical clustering

Usage

hclustering(data, k = NULL, nclumax = 10, labels = NULL, linkage = "ward.D")

Arguments

data	numeric data frame.
k	integer, number of clusters.
nclumax	integer, maximum number of clusters (when k=NULL).
labels	character, row labels.
linkage	character, the agglomeration method to be used in hclust (see method in hclust).

Details

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

Value

A hclustering object.

If k is NULL, the hclustering 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 hclustering object is a list of 5 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)

• Hclust an object of class hclust, see hclust

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.hclustering, hclust

Examples

data <- with(Pbox, data.frame(PTS, P3M, REB=OREB+DREB, AST, TOV, STL, BLK, PF))
data <- subset(data, Pbox$MIN >= 1500)
ID <- Pbox$Player[Pbox$MIN >= 1500]
hclu1 <- hclustering(data)
plot(hclu1)
hclu2 <- hclustering(data, labels=ID, k=7)
plot(hclu2)

sndmrc/BasketAnalyzeR documentation built on June 6, 2023, 12:52 a.m.