index.G2 {clusterSim} | R Documentation |
Calculates G2 internal cluster quality index - Baker & Hubert adaptation of Goodman & Kruskal's Gamma statistic
index.G2(d,cl)
d |
'dist' object |
cl |
A vector of integers indicating the cluster to which each object is allocated |
See file $R_HOME\library\clusterSim\pdf\indexG2_details.pdf for further details
calculated G2 index
Marek Walesiak marek.walesiak@ue.wroc.pl, Andrzej Dudek andrzej.dudek@ue.wroc.pl
Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/clusterSim
Everitt, B.S., Landau, E., Leese, M. (2001), Cluster analysis, Arnold, London, p. 104.
Gatnar, E., Walesiak, M. (Eds.) (2004), Metody statystycznej analizy wielowymiarowej w badaniach marketingowych [Multivariate statistical analysis methods in marketing research], Wydawnictwo AE, Wroclaw, p. 339.
Gordon, A.D. (1999), Classification, Chapman & Hall/CRC, London, p. 62.
Hubert, L. (1974), Approximate evaluation technique for the single-link and complete-link hierarchical clustering procedures, "Journal of the American Statistical Association", vol. 69, no. 347, 698-704.
Milligan, G.W., Cooper, M.C. (1985), An examination of procedures of determining the number of cluster in a data set, "Psychometrika", vol. 50, no. 2, 159-179.
index.G1
, index.G3
, index.S
, index.H
,
index.KL
, index.Gap
, index.DB
# Example 1 library(clusterSim) data(data_ratio) d <- dist.GDM(data_ratio) c <- pam(d, 5, diss = TRUE) icq <- index.G2(d,c$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))