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It was found in this stackoverflow question: https://stackoverflow.com/questions/20416944/parallel-k-means-in-r
R package can be found here: https://cran.r-project.org/web/packages/knor/index.html
It is based on this research paper: https://arxiv.org/abs/1606.08905
From abstract
k-means is one of the most influential and utilized machine learning algorithms. Its computation limits the performance and scalability of many statistical analysis and machine learning tasks. We rethink and optimize k-means in terms of modern NUMA architectures to develop a novel parallelization scheme that delays and minimizes synchronization barriers. The k-means NUMA Optimized Routine
knorlibrary has (i) in-memoryknori, (ii) distributed memoryknord, and (iii) semi-external memoryknorsmodules that radically improve the performance of k-means for varying memory and hardware budgets.knoriboosts performance for single machine datasets by an order of magnitude or more.knorsimproves the scalability of k-means on a memory budget using SSDs.knorsscales to billions of points on a single machine, using a fraction of the resources that distributed in-memory systems require.knordretainsknori's performance characteristics, while scaling in-memory through distributed computation in the cloud.knormodifies Elkan's triangle inequality pruning algorithm such that we utilize it on billion-point datasets without the significant memory overhead of the original algorithm. We demonstrateknoroutperforms distributed commercial products like H2O, Turi (formerly Dato, GraphLab) and Spark's MLlib by more than an order of magnitude for datasets of 107 to 109 points.