title: Kmeans clustering time on scikit
body: <p>How much time should it take to cluster a set of 100'000 L2 normalized 2048-dim feature vectors using k-means with 200 clusters? I have all my data in a huge numpy array, maybe there's a more appropriate data structure?</p>
  
  <p>It didn't seem to do any progress in an hour. I'm also inclining to use the threshold stopping criteria, but it seems to take more than 5 minutes for just 2 iterations. Is there some sort of verbose command I can use to check in on the progress for kmeans clustering on scikit-learn? Does anyone suggest any other approach? Maybe something like dimensionality reduction, or PCA and then kmeans? (I'm just throwing random ideas out there)</p>