clusterSim (version 0.47-4)

index.G3: Calculates G3 internal cluster quality index

Description

Calculates G3 Hubert & Levine internal cluster quality index

Usage

index.G3(d,cl)

Arguments

d

'dist' object

cl

A vector of integers indicating the cluster to which each object is allocated

Value

calculated G3 index

Details

See file \$R\_HOME\library\clusterSim\pdf\indexG3\_details.pdf for further details

References

Gatnar, E., Walesiak, M. (Eds.) (2004), Metody statystycznej analizy wielowymiarowej w badaniach marketingowych [Multivariate statistical analysis methods in marketing research], Wydawnictwo AE, Wroclaw, p. 339. Available at: http://keii.ue.wroc.pl/pracownicy/mw/2004_Gatnar_Walesiak_Metody_SAW_w_badaniach_marketingowych.pdf.

Gordon, A.D. (1999), Classification, Chapman & Hall/CRC, London, p. 62. ISBN 9781584880134.

Milligan, G.W., Cooper, M.C. (1985), An examination of procedures of determining the number of cluster in a data set, "Psychometrika", vol. 50, no. 2, 159-179. Available at: https://dx.doi.org/10.1007/BF02294245.

See Also

index.G1, index.G2, index.S, index.H, index.KL, index.Gap, index.DB

Examples

# NOT RUN {
# Example 1
library(clusterSim)
data(data_ratio)
d <- dist.GDM(data_ratio)
c <- pam(d, 5, diss = TRUE)
icq <- index.G3(d,c$clustering)
print(icq)

# Example 2
library(clusterSim)
data(data_ordinal)
d <- dist.GDM(data_ordinal, method="GDM2")
# nc - number_of_clusters
min_nc=2
max_nc=6
res <- array(0,c(max_nc-min_nc+1, 2))
res[,1] <- min_nc:max_nc
clusters <- NULL
for (nc in min_nc:max_nc)
{
hc <- hclust(d, method="complete")
cl2 <- cutree(hc, k=nc)
res[nc-min_nc+1,2] <- G3 <- index.G3(d,cl2)
clusters <- rbind(clusters,cl2)
}
print(paste("min G3 for",(min_nc:max_nc)[which.min(res[,2])],"clusters=",min(res[,2])))
print("clustering for min G3")
print(clusters[which.min(res[,2]),])
write.table(res,file="G3_res.csv",sep=";",dec=",",row.names=TRUE,col.names=FALSE)
plot(res, type="p", pch=0, xlab="Number of clusters", ylab="G3", xaxt="n")
axis(1, c(min_nc:max_nc))
# }