intCriteria | R Documentation |
intCriteria
calculates various internal clustering validation or
quality criteria.
intCriteria(traj, part, crit)
traj |
|
part |
|
crit |
|
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.
A list containing the computed criteria, in the same order as in the
crit
argument.
Bernard Desgraupes
bernard.desgraupes@u-paris10.fr
University of Paris Ouest - Nanterre
Lab Modal'X (EA 3454)
See the bibliography at the end of the vignette.
getCriteriaNames
, extCriteria
,
bestCriterion
.
# 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"))
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