Can anyone explain how the k-means clustering algorithm converges on distributed systems? It seems that each node in our hadoop cluster would simply find a local optimum. How do we update across multiple nodes?
1 Answer
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At each iteration, local results are merged on a central node. You do not independently run k-means on each node! The data volume is constant in the data set size, and the CPU cost is neglible, so this is very favorable. k-means is embarassingly parallel, and all the advanced methos aim at achieving sublinear processing time.
answered Jan 4, 2015 at 22:12