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Huge, if true. Researchers included a link to (C++) source code: http://research.csc.ncsu.edu/nc-caps/yykmeans.tar.bz2 http://research.csc.ncsu.edu/nc-caps/yy

• ogrisel 11y ago
  I agree it's useful although there also exists a more scalable approximate variant of classical k-Means and the new Yinyang k-Means (referenced in the paper), namely Mini-batch k-Means: http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf This approximate method is implemented (at least) in sofia-ml and scikit-learn.
  • thecopy 11y ago
    Another approach is to transform the dataset into a smaller but representative dataset, called a core-set, and running k-means on that core-set instead which produces a "(1+e )-approximation for the optimal cluster centers". See http://people.csail.mit.edu/dannyf/kmeancoreset.pdf http://people.csail.mit.edu/dannyf/kmeancoreset.pdf