Last data update: 2014.03.03
R: Calculates G3 internal cluster quality index
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
Details
See file $R_HOMElibraryclusterSimpdfindexG3_details.pdf for further details
Value
calculated G3 index
Author(s)
Marek Walesiak marek.walesiak@ue.wroc.pl , Andrzej Dudek andrzej.dudek@ue.wroc.pl
Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/clusterSim
References
Gatnar, E., Walesiak, M. (Eds.) (2004), Metody statystycznej analizy wielowymiarowej w badaniach marketingowych [Multivariate statistical analysis methods in marketing research] , Wydawnictwo AE, Wroclaw, p. 339.
Gordon, A.D. (1999), Classification , Chapman & Hall/CRC, London, p. 62.
Milligan, G.W., Cooper, M.C. (1985), An examination of procedures of determining the number of cluster in a data set , "Psychometrika", vol. 50, no. 2, 159-179.
See Also
index.G1
, index.G2
, index.S
,
index.H
, index.KL
, index.Gap
, index.DB
Examples
# Example 1
library(clusterSim)
data(data_ratio)
d <- dist.GDM(data_ratio)
c <- pam(d, 5, diss = TRUE)
icq <- index.G3(d,c$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))
Results