index.S {clusterSim} | R Documentation |
Calculates Rousseeuw's Silhouette internal cluster quality index
index.S(d,cl)
d |
'dist' object |
cl |
A vector of integers indicating the cluster to which each object is allocated |
See file $R_HOME\library\clusterSim\pdf\indexS_details.pdf for further details
calculated Silhouette index
Marek Walesiak marek.walesiak@ue.wroc.pl, Andrzej Dudek andrzej.dudek@ue.wroc.pl
Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/clusterSim
Gatnar, E., Walesiak, M. (Eds.) (2004), Metody statystycznej analizy wielowymiarowej w badaniach marketingowych [Multivariate statistical analysis methods in marketing research], Wydawnictwo AE, Wroclaw, 342-343, erratum.
Kaufman, L., Rousseeuw, P.J. (1990), Finding groups in data: an introduction to cluster analysis, Wiley, New York, 83-88.
index.G1
, index.G2
, index.G3
,
index.KL
, index.H
, index.Gap
, index.DB
# Example 1 library(clusterSim) data(data_ratio) d <- dist.GDM(data_ratio) c <- pam(d, 5, diss = TRUE) icq <- index.S(d,c$clustering) print(icq) # Example 2 library(clusterSim) data(data_ratio) md <- dist(data_ratio, method="manhattan") # 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] <- S <- index.S(md,cl2$cluster) clusters <- rbind(clusters, cl2$cluster) } print(paste("max S for",(min_nc:max_nc)[which.max(res[,2])],"clusters=",max(res[,2]))) print("clustering for max S") print(clusters[which.max(res[,2]),]) write.table(res,file="S_res.csv",sep=";",dec=",",row.names=TRUE,col.names=FALSE) plot(res,type="p",pch=0,xlab="Number of clusters",ylab="S",xaxt="n") axis(1, c(min_nc:max_nc))