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This study introduces a new clustering algorithm for machine online learning, enhancing the distributed EM algorithm

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Area of Science:

  • Machine Learning
  • Data Mining
  • Computer Science

Background:

  • Traditional machine learning methods face challenges with real-time performance and accuracy in distributed environments.
  • Existing methods struggle to process large volumes of social information efficiently.
  • The greedy EM algorithm is important but has limitations in handling extensive data loads.

Purpose of the Study:

  • To improve the real-time performance and accuracy of the distributed EM algorithm for machine online learning.
  • To propose a clustering analysis algorithm based on distance measurement.
  • To address the limitations of existing methods in loading large amounts of social information.

Main Methods:

  • Developed a Hadoop cluster for clustering the Gaussian mixture model.
  • Compared the running time of the distributed EM algorithm and the greedy algorithm.
  • Assessed algorithm accuracy and scalability by increasing the number of nodes.
  • Explored the visualization of sports movements for enhanced physical education teaching.

Main Results:

  • The proposed algorithm demonstrated improved accuracy and efficiency in clustering.
  • The Hadoop cluster facilitated the processing of large datasets.
  • Visualization of sports movements was shown to increase student interest and improve classroom efficiency.

Conclusions:

  • The developed clustering algorithm enhances the distributed EM algorithm for machine online learning.
  • Hadoop clusters offer an efficient solution for processing large-scale data in machine learning.
  • Visualized sports movement teaching is an effective method for engaging students in physical education.