Unsupervised Machine Learning in R: K-Means

July 28, 2019
By

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K-Means clustering is unsupervised machine learning because there is not a target variable. Clustering can be used to create a target variable, or simply group data by certain characteristics.

Here’s a great and simple way to use R to find clusters, visualize and then tie back to the data source to implement a marketing strategy.

setwd
#import dataset
ABC <-read.table("AbcBank.csv",header=TRUE, 
                  sep=",")

#choose variables to be clustered 
# make sure to exclude ID fields or Dates
ABC_num<- ABC[,2:5]
#scale the data! so they are all normalized 
ABC_scaled <-as.data.frame(scale(ABC_num))

#kmeans function
k3<- kmeans(ABC_scaled, centers=3, nstart=25)
#library with the visualization
library(factoextra)
fviz_cluster(k3, data=ABC_scaled,
             ellipse.type="convex",
             axes =c(1,2),
             geom="point",
             label="none",
             ggtheme=theme_classic())
#check out the centers 
# remember these are normalized but 
#higher values are higher values for the original data
    k3$centers          
#add the cluster to the original dataset!
    ABC$Cluster<-as.numeric(k3$cluster)
    

Check out our awesome clusters:

Repo here with dataset: https://github.com/emileemc/kmeans

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