# NOT RUN {
##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
a<-runif(500, min=3.5, max=2000)
b<-runif(500, min=1.5, max=2000)
df = data.frame(a, b)
#Specifying 4 clusters
results.hkclust<-hkclustering(df,4,100)
centroidssummary(results.hkclust)
with(results.hkclust, pairs(results.hkclust[,1:2], col=c(1:10)[results.hkclust[,3]]))
## The function is currently defined as
function (df, numbk, t)
{
scaled.df <- scale(df)
rm(.Random.seed, envir = globalenv())
temp <- kmeans(scaled.df, numbk)
c <- temp$centers
c <- temp$centers
for (i in 2:t) {
rm(.Random.seed, envir = globalenv())
temp <- kmeans(scaled.df, numbk)
c <- rbind(c, temp$centers)
}
cr <- as.data.frame(c, row.names = F)
d <- dist(cr, method = "euclidean")
fit <- hclust(d, method = "centroid")
cr$clusnumber <- cutree(fit, k = numbk)
centroids1 <- aggregate(cr, by = list(cr$clusnumber), FUN = mean)
centr <- centroids1[, c(2:(length(df) + 1))]
final <- kmeans(scaled.df, centr)
clustereddata <- cbind(df, final$cluster)
colnames(clustereddata)[(length(df) + 1)] <- "cluster_number"
return(clustereddata)
}
# }
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