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k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.

https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/

data(iris)
summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

Including Plots

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## Warning: package 'ggplot2' was built under R version 3.4.3

irisCluster<-kmeans(iris[,3:4],3,nstart=30) #nstart=number of starting points tried to arrive at final
irisCluster
## K-means clustering with 3 clusters of sizes 50, 52, 48
## 
## Cluster means:
##   Petal.Length Petal.Width
## 1     1.462000    0.246000
## 2     4.269231    1.342308
## 3     5.595833    2.037500
## 
## Clustering vector:
##   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
##  [71] 2 2 2 2 2 2 2 3 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3
## [106] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3
## [141] 3 3 3 3 3 3 3 3 3 3
## 
## Within cluster sum of squares by cluster:
## [1]  2.02200 13.05769 16.29167
##  (between_SS / total_SS =  94.3 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"    
## [5] "tot.withinss" "betweenss"    "size"         "iter"        
## [9] "ifault"
table(irisCluster$cluster,iris$Species)
##    
##     setosa versicolor virginica
##   1     50          0         0
##   2      0         48         4
##   3      0          2        46

Conceptually, factors are variables in R which take on a limited number of different values; such variables are often refered to as categorical variables. One of the most important uses of factors is in statistical modeling; since categorical variables enter into statistical models differently than continuous variables, storing data as factors insures that the modeling functions will treat such data correctly.

irisCluster$cluster<-as.factor(irisCluster$cluster) #continous to discrete
ggplot(iris,aes(Petal.Length,Petal.Width, color=irisCluster$cluster))+geom_point()

Elbow Method WSS=Total within-cluster sum of squares(distance) Take elbow or kink point beyond which wss changes little as optimal number of clusters

wss<-sapply(1:15,function(k){kmeans(iris[,3:4],k,nstart=30,iter.max = 20)$tot.withinss})
wss
##  [1] 550.895333  86.390220  31.371359  19.465989  13.916909  11.025145
##  [7]   9.236596   7.674414   6.456495   5.550520   5.088786   4.750035
## [13]   4.241221   3.927031   3.519928
plot(1:15,wss)

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.