k-means: Same clusters for every execution

问题:

Is it possible to get same kmeans clusters for every execution for a particular data set. Just like for a random value we can use a fixed seed. Is it possible to stop randomness for clustering?

回答1:

Yes. Use set.seed to set a seed for the random value before doing the clustering.

Using the example in kmeans:

set.seed(1)
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")


set.seed(2)
XX <- kmeans(x, 2)

set.seed(2)
YY <- kmeans(x, 2)

Test for equality:

identical(XX, YY)
[1] TRUE

回答2:

Yes, calling set.seed(foo) immediately prior to running kmeans(....) will give the same random start and hence the same clustering each time. foo is a seed, like 42 or some other numeric value.