# R/index.G2.r In clusterSim: Searching for Optimal Clustering Procedure for a Data Set

#### Documented in index.G2

```index.G2 <- function(d,cl){
cn <- max(cl)
n <- length(cl)
dmat <- as.matrix(d)
diameter <- average.distance <- median.distance <- separation <-
average.toother <-
cluster.size <- within.dist <- between.dist <- numeric(0)
separation.matrix <- matrix(0,ncol=cn,nrow=cn)
di <- list()
for (i in 1:cn){
cluster.size[i] <- sum(cl==i)
#print(i)
#print(cl==i)
#print(dmat[cl==i,cl==i])
di <- as.dist(dmat[cl==i,cl==i])
within.dist <- c(within.dist,di)
#diameter[i] <- max(di)
average.distance[i] <- mean(di)
median.distance[i] <- median(di)
bv <- numeric(0)
for (j in 1:cn){
if (j!=i){
sij <- dmat[cl==i,cl==j]
bv <- c(bv,sij)
if (i<j){
separation.matrix[i,j] <- separation.matrix[j,i] <- min(sij)
between.dist <- c(between.dist,sij)
}
}
}
}
nwithin<-length(within.dist)
nbetween<-length(between.dist)
.C("fng2",as.double(within.dist),as.integer(nwithin),as.double(between.dist),as.integer(nbetween),wynik=double(1),PACKAGE="clusterSim")\$wynik
}
```

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clusterSim documentation built on Jan. 8, 2021, 2:13 a.m.