clusterSim (version 0.51-3)

index.G2: Calculates G2 internal cluster quality index

Description

Calculates G2 internal cluster quality index - Baker & Hubert adaptation of Goodman & Kruskal's Gamma statistic

Usage

index.G2(d,cl)

Value

calculated G2 index

Arguments

d

'dist' object

cl

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

Author

Marek Walesiak , Andrzej Dudek

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/clusterSim/

Details

See file $R_HOME\library\clusterSim\pdf\indexG2_details.pdf for further details

References

Everitt, B.S., Landau, E., Leese, M. (2001), Cluster analysis, Arnold, London, p. 104. ISBN 9780340761199.

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.

Hubert, L. (1974), Approximate evaluation technique for the single-link and complete-link hierarchical clustering procedures, "Journal of the American Statistical Association", vol. 69, no. 347, 698-704. Available at: tools:::Rd_expr_doi("10.1080/01621459.1974.10480191").

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: tools:::Rd_expr_doi("10.1007/BF02294245").

See Also

index.G1, index.G3, index.S, index.H, index.KL, index.Gap, index.C, index.DB

Examples

# Example 1
library(clusterSim)
data(data_ratio)
d <- dist.GDM(data_ratio)
c <- pam(d, 5, diss = TRUE)
icq <- index.G2(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)
{
  cl2 <- pam(d, nc, diss=TRUE)
  res[nc-min_nc+1,2] <- G2 <- index.G2(d,cl2$cluster)
  clusters <- rbind(clusters,cl2$cluster)
}
print(paste("max G2 for",(min_nc:max_nc)[which.max(res[,2])],"clusters=",max(res[,2])))
print("clustering for max G2")
print(clusters[which.max(res[,2]),])
plot(res, type="p", pch=0, xlab="Number of clusters", ylab="G2", xaxt="n")
axis(1, c(min_nc:max_nc))

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