Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

intCriteria

Compute internal clustering criteria

Description

intCriteria calculates various internal clustering validation or quality criteria.

Usage

intCriteria(traj, part, crit)

Arguments

traj

[matrix] : the matrix of observations (trajectories).
part

[vector] : the partition vector.
crit

[vector] : a vector containing the names of the indices to compute.

Details

The function intCriteria calculates internal clustering indices. The list of all the supported criteria can be obtained with the getCriteriaNames function.

The currently available indices are :

  • "Ball_Hall"

  • "Banfeld_Raftery"

  • "C_index"

  • "Calinski_Harabasz"

  • "Davies_Bouldin"

  • "Det_Ratio"

  • "Dunn"

  • "Gamma"

  • "G_plus"

  • "GDI11"

  • "GDI12"

  • "GDI13"

  • "GDI21"

  • "GDI22"

  • "GDI23"

  • "GDI31"

  • "GDI32"

  • "GDI33"

  • "GDI41"

  • "GDI42"

  • "GDI43"

  • "GDI51"

  • "GDI52"

  • "GDI53"

  • "Ksq_DetW"

  • "Log_Det_Ratio"

  • "Log_SS_Ratio"

  • "McClain_Rao"

  • "PBM"

  • "Point_Biserial"

  • "Ray_Turi"

  • "Ratkowsky_Lance"

  • "Scott_Symons"

  • "SD_Scat"

  • "SD_Dis"

  • "S_Dbw"

  • "Silhouette"

  • "Tau"

  • "Trace_W"

  • "Trace_WiB"

  • "Wemmert_Gancarski"

  • "Xie_Beni"

All the names are case insensitive and can be abbreviated. The keyword "all" can also be used as a shortcut to calculate all the internal indices.

The GDI (Generalized Dunn Indices) are designated by the following convention: GDImn, where the integers m (1<=m<=5) and n (1<=n<=3) correspond to the between-group and within-group distances respectively. See the vignette for a comprehensive definition of the various distances. GDI alone is synonym of GDI11 and is the genuine Dunn's index.

Value

A list containing the computed criteria, in the same order as in the crit argument.

Author

Bernard Desgraupes
bernard.desgraupes@u-paris10.fr
University of Paris Ouest - Nanterre
Lab Modal'X (EA 3454)

References

See the bibliography at the end of the vignette.

See Also

Examples

# Create some data
x <- rbind(matrix(rnorm(100, mean = 0, sd = 0.5), ncol = 2),
           matrix(rnorm(100, mean = 1, sd = 0.5), ncol = 2),
           matrix(rnorm(100, mean = 2, sd = 0.5), ncol = 2))
# Perform the kmeans algorithm
cl <- kmeans(x, 3)
# Compute all the internal indices
intCriteria(x,cl$cluster,"all")
# Compute some of them
intCriteria(x,cl$cluster,c("C_index","Calinski_Harabasz","Dunn"))
# The names are case insensitive and can be abbreviated
intCriteria(x,cl$cluster,c("det","cal","dav"))
clusterCrit

Clustering Indices

v1.2.8
GPL (>= 2)
Authors
Bernard Desgraupes (University of Paris Ouest - Lab Modal'X)
Initial release
2018-07-26

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.