K mean clustering crime data

KData <- read.csv("C:\\Users\\Harisha\\Desktop\\Datascience Assignments\\K mean clustering\\crime_data.csv")


attach(KData)

attach(KData)

## The following objects are masked from KData (pos = 3):
## 
##     Assault, Murder, Rape, UrbanPop, X

Data <- scale(KData[,2:5])

View(Data)
plot(Data)

km <- kmeans(Data,4)

str(km)

## List of 9
##  $ cluster     : int [1:50] 1 2 2 1 2 2 4 4 2 1 ...
##  $ centers     : num [1:4, 1:4] 1.412 0.695 -0.962 -0.489 0.874 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:4] "1" "2" "3" "4"
##   .. ..$ : chr [1:4] "Murder" "Assault" "UrbanPop" "Rape"
##  $ totss       : num 196
##  $ withinss    : num [1:4] 8.32 19.92 11.95 16.21
##  $ tot.withinss: num 56.4
##  $ betweenss   : num 140
##  $ size        : int [1:4] 8 13 13 16
##  $ iter        : int 2
##  $ ifault      : int 0
##  - attr(*, "class")= chr "kmeans"

km$cluster

##  [1] 1 2 2 1 2 2 4 4 2 1 4 3 2 4 3 4 3 1 3 2 4 2 3 1 2 3 3 2 3 4 2 2 1 3 4
## [36] 4 4 4 4 1 3 1 2 4 3 4 4 3 3 4

Final <- data.frame(km$cluster,KData)

View(Final)

x <- aggregate(KData[,2:5],by=list(km$cluster),FUN = mean)
x

##   Group.1   Murder   Assault UrbanPop     Rape
## 1       1 13.93750 243.62500 53.75000 21.41250
## 2       2 10.81538 257.38462 76.00000 33.19231
## 3       3  3.60000  78.53846 52.07692 12.17692
## 4       4  5.65625 138.87500 73.87500 18.78125