clusterCrit (version 1.3.0)

intCriteria: Compute internal clustering criteria

Description

intCriteria calculates various internal clustering validation or quality criteria.

Usage

intCriteria(traj, part, crit)

Value

A list containing the computed criteria, in the same order as in the

crit argument.

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.

Author

Bernard Desgraupes

University of Paris Ouest - Nanterre
Lab Modal'X (EA 3454)

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.

References

See the bibliography at the end of the vignette.

See Also

getCriteriaNames, extCriteria, bestCriterion.

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"))