# Unsupervised Machine Learning in R: K-Means

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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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