Be the first to like this
Ted Dunning describes an implementation of recent results that provide high quality k-means clustering at very high speed.
"For well clusterable data, this algorithm provides good bounds on quality, but practically speaking, it makes clustering practical in many applications by providing roughly 3 orders of magnitude speedup relative to the standard algorithm based on Lloyd's initial efforts. In addition, the algorithm is highly amenable to implementation using map-reduce and shows essentially linear speedup.
Just as significant, this new algorithm allows clustering with a very large number of clusters which makes it practical to use as a feature extraction algorithm or set up for a nearest neighbor search. " - Ted Dunning