```
# NOT RUN {
require(graphics)
# a 2-dimensional example
x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2),
matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2))
colnames(x) <- c("x", "y")
(cl <- kmeans(x, 2))
plot(x, col = cl$cluster)
points(cl$centers, col = 1:2, pch = 8, cex = 2)
# sum of squares
ss <- function(x) sum(scale(x, scale = FALSE)^2)
## cluster centers "fitted" to each obs.:
fitted.x <- fitted(cl); head(fitted.x)
resid.x <- x - fitted(cl)
## Equalities : ----------------------------------
cbind(cl[c("betweenss", "tot.withinss", "totss")], # the same two columns
c(ss(fitted.x), ss(resid.x), ss(x)))
stopifnot(all.equal(cl$ totss, ss(x)),
all.equal(cl$ tot.withinss, ss(resid.x)),
## these three are the same:
all.equal(cl$ betweenss, ss(fitted.x)),
all.equal(cl$ betweenss, cl$totss - cl$tot.withinss),
## and hence also
all.equal(ss(x), ss(fitted.x) + ss(resid.x))
)
kmeans(x,1)$withinss # trivial one-cluster, (its W.SS == ss(x))
## random starts do help here with too many clusters
## (and are often recommended anyway!):
(cl <- kmeans(x, 5, nstart = 25))
plot(x, col = cl$cluster)
points(cl$centers, col = 1:5, pch = 8)
# }
```

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