Nothing

```
print.tuneclus <- function(x, ...) {
size = x$clusobjbest$size
csize = round((table(x$clusobjbest$cluster)/sum(table(x$clusobjbest$cluster)))*100,digits=1)
k = x$nclusbest
d = x$ndimbest
x$clusobjbest$centroid = data.frame(x$clusobjbest$centroid)
cluspca = FALSE
try(if (class(x$clusobjbest)=="cluspca") { cluspca = TRUE }, silent = TRUE)
if (cluspca == TRUE) {
if (x$clusobjbest$center == TRUE) {
centering = "mean centered" } else {
centering = "not centered"
}
if (x$clusobjbest$scale == TRUE) {
scaling = "standardized" } else {
scaling = "unstandardized"
}
tt = paste('(',csize,'%)',sep="")
cs = paste(size, tt, sep = " ", collapse = ", ")
if (x$crit== "asw")
cat(paste("\nThe best solution was obtained for ",k ," clusters of sizes ", paste(cs, collapse = ", ")," in ",d ," dimensions, for an average Silhouette width value of ",round(x$critbest,3), ". Variables were ", centering, " and ", scaling,".", "\n", sep = ""))
else if (x$crit== "ch")
cat(paste("\nThe best solution was obtained for ",k ," clusters of sizes ", paste(cs, collapse = ", ")," in ",d ," dimensions, for a Calinski-Harabasz value of ",round(x$critbest,3), ". Variables were ", centering, " and ", scaling,".", "\n", sep = ""))
else
cat(paste("\nThe best solution was obtained for ",k ," clusters of sizes ", paste(cs, collapse = ", ")," in ",d ," dimensions, for an objective criterion value of ",round(x$critbest,3), ". Variables were ", centering, " and ", scaling,".", "\n", sep = ""))
} else {
tt = paste('(',csize,'%)',sep="")
cs = paste(size, tt, sep = " ", collapse = ", ")
if (x$crit== "asw")
cat(paste("\nThe best solution was obtained for ",k ," clusters of sizes ", paste(cs, collapse = ", ")," in ",d ," dimensions, for an average Silhouette width value of ",round(x$critbest,3), ".", "\n", sep = ""))
else if (x$crit== "ch")
cat(paste("\nThe best solution was obtained for ",k ," clusters of sizes ", paste(cs, collapse = ", ")," in ",d ," dimensions, for a Calinski-Harabasz value of ",round(x$critbest,3), ".", "\n", sep = ""))
else
cat(paste("\nThe best solution was obtained for ",k ," clusters of sizes ", paste(cs, collapse = ", ")," in ",d ," dimensions, for an objective criterion value of ",round(x$critbest,3), ".", "\n", sep = ""))
}
cat("\nCluster quality criterion values across the specified range of clusters (rows) and dimensions (columns):\n")
print(x$critgrid)
if (x$crit== "asw") {
cat("\nThe average Silhouette width values of each cluster are:\n")
print(as.vector(round(x$cluasw,2)))
}
cat("\nCluster centroids:\n")
xcent = data.frame(round(x$clusobjbest$centroid,4))
for (i in 1:k) {
rownames(xcent)[i] = paste("Cluster",i)
}
for (i in 1:ncol(xcent)) {
colnames(xcent)[i] = paste0("Dim.",i)
}
print(xcent)
# attc = data.frame(round(x$clusobjbest$attcoord,4))
# cat("\nVariable scores:\n")
# for (i in 1:ncol(attc)) {
# colnames(attc)[i] = paste0("Dim.",i)
# }
# print(attc)
cat("\nWithin cluster sum of squares by cluster:\n")
#resid <- x$obscoord - fitted(x)
#tot.withinss <- ss(resid)
#print(tot.withinss)
betweenss <- ss(x$clusobjbest$centroid[x$clusobjbest$cluster,]) # or
#betweenss <- ss(fitted(x))
withinss <- sapply(split(as.data.frame(x$clusobjbest$obscoord), x$clusobjbest$cluster), ss)
print(as.vector(round(withinss,4)))
#tot.withinss <- sum(withinss) # or
totss <- ss(x$clusobjbest$obscoord) # or tot.withinss + betweenss
cat(" (between_SS / total_SS = ",round((betweenss/totss)*100,2),"%)","\n")
cat(paste("\nObjective criterion value:",round(x$clusobjbest$criterion,4),"\n"))
cat("\nAvailable output:\n",
sep = "\n")
print(names(x))
invisible(x)
}
ss <- function(x) sum(scale(x, scale = FALSE)^2)
```

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