index.G2: Calculates G2 internal cluster quality index

In clusterSim: Searching for Optimal Clustering Procedure for a Data Set

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)

Arguments

d	'dist' object
cl	A vector of integers indicating the cluster to which each object is allocated

Details

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

Value

calculated G2 index

Author(s)

Marek Walesiak [e-mail], Andrzej Dudek [e-mail]

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland

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.

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: \Sexpr[results=rd]{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: \Sexpr[results=rd]{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))

clusterSim documentation built on Sept. 30, 2024, 9:15 a.m.