R: Calculates Hubert & Levin C index - internal cluster quality...

index.C {clusterSim} | R Documentation

## Calculates Hubert & Levin C index - internal cluster quality index

### Description

Calculates Hubert & Levin C index - internal cluster quality index

### Usage

    index.C(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\indexC\_details.pdf for further details

Thanks to Özge Sahin from Technical University of Munich for for pointing the difference between `index.G3` and `index.C`.

### Value

calculated C 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

Hubert, L.J., Levin, J.R. (1976), A General Statistical Framework for Assessing Categorical Clustering in Free Recall, Psychological Bulletin, Vol. 83, No. 6, 1072-1080.

### See Also

`index.G1`, `index.G2`, `index.G3`, `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.C(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] <- C <- index.C(d,cl2)
    clusters <- rbind(clusters,cl2)
    }
    print(paste("min C for",(min_nc:max_nc)[which.min(res[,2])],"clusters=",min(res[,2])))
    print("clustering for min C-index")
    print(clusters[which.min(res[,2]),])
    #write.table(res,file="C_res.csv",sep=";",dec=",",row.names=TRUE,col.names=FALSE)
    plot(res, type="p", pch=0, xlab="Number of clusters", ylab="C", xaxt="n")
    axis(1, c(min_nc:max_nc))

[Package clusterSim version 0.49-2 Index]