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Updated: Oct 20, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Basketball Data Analysis Using Spark Framework and K-Means Algorithm.

Xijun Hong1

  • 1College of Sports Science, Dali University, Dali 671003, Yunnan, China.

Journal of Healthcare Engineering
|September 17, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a big data framework for basketball training analysis using Spark and cuckoo search clustering. The approach enhances player performance assessment, particularly in defensive situations and shooting training.

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Area of Science:

  • Sports Science and Analytics
  • Data Science and Big Data
  • Computational Intelligence

Background:

  • Wearable and sensing technologies generate vast amounts of sports data, creating challenges for traditional analysis.
  • Big data analytics are crucial for improving the effectiveness of basketball training and player evaluation.
  • Assessing defensive performance under high-pressure situations is vital for team strategy.

Purpose of the Study:

  • To propose an efficient big data processing framework for basketball training analysis.
  • To enhance player performance assessment using advanced algorithms and distributed computing.
  • To provide tools for recruiters and trainers to evaluate and improve team performance.

Main Methods:

  • Implementation of the Spark framework for in-memory big data processing.
  • Utilization of a cuckoo search algorithm for swarm intelligence optimization.
  • Application of K-clustering algorithm within a Spark distributed environment for enhanced analysis.
  • Examination of defensive performance metrics in high-pressure game scenarios.

Main Results:

  • The proposed approach demonstrates superior clustering performance and practical utility compared to existing methods.
  • The framework effectively analyzes player defensive performance under pressure.
  • Significant improvements were observed in assessing the impact of moving and shooting training.

Conclusions:

  • The developed big data framework offers a powerful tool for basketball analytics.
  • This method aids in understanding player qualities and optimizing team strategies.
  • The findings support enhanced training effectiveness, particularly in shooting and defensive skills.