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.
# 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"))