Scaling GMM Expectation Maximization algorithm using bulk synchronous Parallel approach

We have provided a parallel implementation of Gaussian Mixture Model (GMM) Expectation Maximization algorithm using Apache Hama Bulk synchronous Parallel approach. Apache Hama is suitable for iterative, compute intensive tasks. EM is iterative algorithm which converges to local minimum after many iterations. We have provided approach for distributing workload for Expectation and Maximization tasks on cluster nodes in case of big data. The approach is compared with Hadoop MaprRduce and Apache Spark implementations, using different datasets.