R: Calculates Calinski-Harabasz pseudo F-statistic

index.G1 {clusterSim} | R Documentation

## Calculates Calinski-Harabasz pseudo F-statistic

### Description

Calculates Calinski-Harabasz pseudo F-statistic

### Usage

    index.G1 (x,cl,d=NULL,centrotypes="centroids")

### Arguments

`x` | data
`cl` | A vector of integers indicating the cluster to which each object is allocated
`d` | optional distance matrix, used for calculations if centrotypes="medoids"
`centrotypes` | "centroids" or "medoids"

### Details

See file ../doc/indexG1_details.pdf for further details.

thank to Nejc Ilc from University of Ljubljana for fixing error for one-element clusters.

### Value

Calinski-Harabasz pseudo F-statistic

### 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

Calinski, T., Harabasz, J. (1974), A dendrite method for cluster analysis, "Communications in Statistics", vol. 3, 1-27. Available at: doi: 10.1080/03610927408827101.

Everitt, B.S., Landau, E., Leese, M. (2001), Cluster analysis, Arnold, London, p. 103. 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. 338. 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: doi: 10.1007/BF02294245.

### See Also

`index.G2`,`index.G3`,`index.S`, `index.C`, `index.H`,`index.KL`,`index.Gap`, `index.DB`

### Examples

    # Example 1
    library(clusterSim)
    data(data_ratio)
    c<- pam(data_ratio,10)
    index.G1(data_ratio,c$clustering)

    # Example 2
    library(clusterSim)
    data(data_ratio)
    md <- dist(data_ratio, method="euclidean")
    # nc - number_of_clusters
    min_nc=2
    max_nc=20
    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(md, nc, diss=TRUE)
    res[nc-min_nc+1,2] <- G1 <- index.G1(data_ratio,cl2$cluster,centrotypes="centroids")
    clusters <- rbind(clusters, cl2$cluster)
    }
    print(paste("max G1 for",(min_nc:max_nc)[which.max(res[,2])],"clusters=",max(res[,2])))
    print("clustering for max G1")
    print(clusters[which.max(res[,2]),])
    #write.table(res,file="G1_res.csv",sep=";",dec=",",row.names=TRUE,col.names=FALSE)
    plot(res, type="p", pch=0, xlab="Number of clusters", ylab="G1", xaxt="n")
    axis(1, c(min_nc:max_nc))

[Package clusterSim version 0.49-2 Index]