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I am actually trying to create a few clusters with my data of about 400 observations, and am a little confused about the role played by the starting number of seeds.
Irrespective of changing my number of starting seeds I end up with the exact same observations in each cluster. Which is why I was trying to understand if starting the analysis with a different value for the number of seeds can actually change my final clusters. And if yes, when does it happen ?
The K-Means algorithm can converge to local minimums depedning on the input points and the starting seed. Having different starting seeds and then picking the `best` K-mean solution is the standard way to work around this. For you specific dataset it might be that it always converges to the same global minimum.