R: Clustering results are different everytime I run

Question

library(amap)
set.seed(5)
Kmeans(mydata, 5, iter.max=500, nstart=1, method=\"euclidean\")

in \'amap\' package and run several times, but even thoug

Answer 1

Just a reminder that K-mean results are sensitive to the order of the data points in the data set. If you run again the proper code with randomized data points you will get a different result

Answer 2

You must be doing something wrong. I get reproducible results each time I run the following code, as long as I set the seed before each call to Kmeans():

library(amap)

out <- vector(mode = "list", length = 10)
for(i in seq_along(out)) {
    set.seed(1)
    out[[i]] <- Kmeans(iris[, -5], 3, iter.max=500, nstart=1, method="euclidean")
}

for(i in seq_along(out[-1])) {
    print(all.equal(out[[i]], out[[i+1]]))
}

The last for loop prints:

[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE

Indicating the results are exactly the same each time.

Answer 3

Have you set the seed? set.seed(1)

Everytime K-Means initializes the centroid, it is generated randomly, which is needing seed for generating random values.