R: Calculates G2 internal cluster quality index

index.G2 {clusterSim} | R Documentation

## 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 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

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: 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: 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]),])
    #write.table(res,file="G2_res.csv",sep=";",dec=",",row.names=TRUE,col.names=FALSE)
    plot(res, type="p", pch=0, xlab="Number of clusters", ylab="G2", xaxt="n")
    axis(1, c(min_nc:max_nc))

[Package clusterSim version 0.49-2 Index]