• DocumentCode
    3729239
  • Title

    Scaling GMM Expectation Maximization algorithm using bulk synchronous Parallel approach

  • Author

    Abhay A. Ratnaparkhi;Emmanuel Pilli;R. C. Joshi

  • Author_Institution
    Department of Computer Science and Engineering, Graphic Era University, Dehradun, India
  • fYear
    2015
  • Firstpage
    558
  • Lastpage
    562
  • Abstract
    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.
  • Keywords
    "Clustering algorithms","Computational modeling","Sparks","Peer-to-peer computing","Synchronization","Machine learning algorithms","Probability"
  • Publisher
    ieee
  • Conference_Titel
    Green Computing and Internet of Things (ICGCIoT), 2015 International Conference on
  • Type

    conf

  • DOI
    10.1109/ICGCIoT.2015.7380527
  • Filename
    7380527
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3729239