hierarchical clustering

data("USArrests")
df <- na.omit(USArrests)
df <- scale(df)
head(df)

##                Murder   Assault   UrbanPop         Rape
## Alabama    1.24256408 0.7828393 -0.5209066 -0.003416473
## Alaska     0.50786248 1.1068225 -1.2117642  2.484202941
## Arizona    0.07163341 1.4788032  0.9989801  1.042878388
## Arkansas   0.23234938 0.2308680 -1.0735927 -0.184916602
## California 0.27826823 1.2628144  1.7589234  2.067820292
## Colorado   0.02571456 0.3988593  0.8608085  1.864967207

d <- dist(df, method = "euclidean")

hc <- hclust(d, method = "ward.D")
plot(hc, cex = 0.6, hang = -1)

hc_d2 <- hclust(d, method = "ward.D2")
plot(hc_d2, cex = 0.6, hang = -1)

hc_single <- hclust(d, method = "single")
plot(hc_single, cex = 0.6, hang = -1)

hc_complete <- hclust(d, method = "complete")
plot(hc_complete, cex = 0.6, hang = -1)

hc_av <- hclust(d, method = "average")
plot(hc_av, cex = 0.6, hang = -1)

hc_mcq <- hclust(d, method = "mcquitty")
plot(hc_mcq, cex = 0.6, hang = -1)

hc_median <- hclust(d, method = "median")
plot(hc_median, cex = 0.6, hang = -1)

hc_centroid <- hclust(d, method = "centroid")
plot(hc_centroid, cex = 0.6, hang = -1)