K-means requires the solution of a non-convex optimization problem. This means that the solution found by any algorithm highly depends on the initialization. You can't expect to get the same results if you don't use the same initialization. Even with the same initialization, some slight differences in the implementation could lead to different results.
HTH, Mathieu On Tue, Jan 6, 2015 at 1:29 PM, Lee S <sle...@gmail.com> wrote: > Hi, I used the synthetic data to test the kmeans method. > And I write the code own to convert center points to sequecefiles. > Then I ran the kmeans with parameter( -i input -o output -c center -x 3 -cd > 1 -cl) , > I compared the dumped clusteredPoints with the result of scikit-learn kmens > result, it's totally different. I'm very confused. > > Does anybody ever run kmeans with center points provided and compare the > result with other ml-library? >
