聚类绘图

To install factoextra, type this: install.packages(“factoextra”).

We’ll use the USArrest data set and we start by standardizing the data:

df <- scale(USArrests)

# Compute hierarchical k-means clustering
library(factoextra)
res.hk <-hkmeans(df, 4)
# Elements returned by hkmeans()
names(res.hk)

##  [1] "cluster"      "centers"      "totss"        "withinss"    
##  [5] "tot.withinss" "betweenss"    "size"         "iter"        
##  [9] "ifault"       "data"         "hclust"

To print all the results, type this:

# Print the results
res.hk

# Visualize the tree
fviz_dend(res.hk, cex = 0.6, palette = "jco", 
          rect = TRUE, rect_border = "jco", rect_fill = TRUE)

# Visualize the hkmeans final clusters
fviz_cluster(res.hk, palette = "jco", repel = TRUE,
             ggtheme = theme_classic())

Summary

We described hybrid hierarchical k-means clustering for improving k-means results.