Kmeans

library(ggplot2)

df <- data.frame(age = c(18, 21, 22, 24, 26, 26, 27, 30, 31, 35, 39, 40, 41, 42, 44, 46, 47, 48, 49, 54),
    spend = c(10, 11, 22, 15, 12, 13, 14, 33, 39, 37, 44, 27, 29, 20, 28, 21, 30, 31, 23, 24))

ggplot(df, aes(x = age, y = spend)) + geom_point()

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CODE: HIERACHICAL CLUSTERING

clusters <- hclust(dist(iris[, 3:4]))

plot(clusters)

clusterCut <- cutree(clusters, 3)

plot(clusterCut,type="p")

table(clusterCut, iris$Species)

ggplot(iris, aes(Petal.Length, Petal.Width, color = iris$Species)) +

  geom_point(alpha = 0.4, size = 3.5) + geom_point(col = clusterCut) +

  scale_color_manual(values = c('black', 'red', 'green'))

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install.packages(“devtools”)

install.packages(“factoextra”)

library(devtools)

library(factoextra)

data("multishapes")

df <- multishapes[, 1:2]

set.seed(123)

km.res <- kmeans(df, 5, nstart = 25)

fviz_cluster(km.res, df, frame = FALSE, geom = "point")