Abstract
Iterative machine learning algorithms, i.e., k-means (KM), expectation maximization (EM), become overwhelmed with big data since all data points are being continually and indiscriminately visited while a cost is being minimized. In this work, we demonstrate (1) an optimization approach to reduce training run-time complexity of iterative machine learning algorithms and (2) implementation of this framework over KM algorithm. We call this extended KM algorithm, KM∗. The experimental results show that KM∗ outperforms KM over big real world and synthetic data sets. Lastly, we demonstrate the theoretical elements of our work.
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