Description Usage Arguments Author(s) Examples
Takes a dataframe and performs kmeans and a hierarchical clustering on the dataframe using teh gap statistic to calculate the initial number of centroids. The function outputs a dataframe as the clustered data
1 | hkclusplus(df, t)
|
df |
original dataframe |
t |
Number of iterations to find the centroids |
Kaloyan S <kaloyanS@profusion.com>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
a<-runif(300, min=3.5, max=2000)
b<-runif(300, min=1.5, max=2000)
df = data.frame(a, b)
#Let the Gap statistic to find the clusters
results.hkplus<-hkclusplus(df,100)
centroidssummary(results.hkplus)
with(results.hkplus, pairs(results.hkplus[,1:2], col=c(1:7)[results.hkplus[,3]]))
## The function is currently defined as
function (df, t)
{
library(cluster)
scaled.df <- scale(df)
numbk <- which.max(clusGap(scaled.df, FUN = kmeans, K.max = 8,
B = 200)$Tab[, 3])
rm(.Random.seed, envir = globalenv())
temp <- kmeans(scaled.df, numbk)
c <- temp$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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