> cal<-c(52, 160, 89, 57, 34, 32, 30, 69)
> car<-c(112.4, 8.5, 22.8, 14.5, 8.2, 7.7, 7.6, 18.1)
> fat<-c(0.2, 14.7, 1.3, 0.7, 0.2, 0.3, 0.2, 0.2)
> pro<-c(0.3, 2.0, 1.1, 0.3, 0.8, 0.7, 0.6, 0.7)
> fib<-c(2.4, 6.7, 2.6, 2.4, 0.9, 2.0, 0.4, 0.9)
> sug<-c(10.4, 0.7, 12.2, 9.9, 7.9, 4.7, 6.2, 15.5)
> fruits<-data.frame(cal, car, fat, pro, fib, sug)
> rownames(fruits)<-c('apple', 'avocado', 'banana', 'blueberry', 'melon', 'watermelon', 'strawberry', 'grape’)
# 표준화
> fruits<-as.data.frame(scale(fruits, center=T, scale=T))
# k-means의 k=3
> result<-kmeans(fruits, centers=3)
> result$centers
cal car fat pro fib sug
1 2.1853644 -0.4605418 2.4675513 2.1738992 2.24305156 -1.6761980
2 -0.3127448 -0.3305558 -0.3445012 -0.2059483 -0.38337331 0.2085093
3 -0.3088956 2.4438765 -0.4005444 -0.9382091 0.05718828 0.4251423
> fruits$cluster<-result$cluster
> head(fruits)
cal car fat pro fib sug
apple -0.3088956 2.4438765 -0.4005444 -0.93820914 0.05718828 0.4251423
avocado 2.1853644 -0.4605418 2.4675513 2.17389922 2.24305156 -1.6761980
banana 0.5456194 -0.0607999 -0.1829647 0.52631244 0.15885634 0.8150817
blueberry -0.1934206 -0.2928179 -0.3016446 -0.93820914 0.05718828 0.3168258
melon -0.7246056 -0.4689280 -0.4005444 -0.02288315 -0.70532216 -0.1164402
watermelon -0.7707956 -0.4829050 -0.3807644 -0.20594835 -0.14614784 -0.8096659
cluster
apple 3
avocado 1
banana 2
blueberry 2
melon 2
watermelon 2