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Web log mining techniques to optimize Apriori association rule algorithm in sports data information management.

Tiantian Li1, Fang Liu2, Xiaobin Chen1

  • 1Department of Sports and Arts, Bengbu Medical University, Bengbu, 233000, Anhui, China.

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This study optimizes college sports data management using an enhanced Apriori algorithm and web technology. The improved system shows higher efficiency and accuracy for sports information retrieval.

Keywords:
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Area of Science:

  • Computer Science
  • Data Mining
  • Sports Analytics

Background:

  • College sports data management systems require optimization for efficiency and accuracy.
  • Traditional data mining algorithms face challenges with increasing data volumes.

Purpose of the Study:

  • To enhance college sports data information management systems.
  • To integrate log mining techniques with the Apriori algorithm for optimized data analysis.

Main Methods:

  • Utilized genetic algorithms and web application development technology.
  • Optimized the Apriori algorithm using novel log mining techniques.
  • Developed and validated an upgraded sports data management system through experiments.

Main Results:

  • Optimized algorithm execution efficiency improved by 10-15% compared to the traditional Apriori algorithm.
  • Achieved an average retrieval accuracy of 98.3% with a 23% increase in retrieval time.
  • Demonstrated significant improvements in handling large volumes of sports data.

Conclusions:

  • The proposed technology and algorithms offer valuable improvements for sports information management systems.
  • The optimized system enhances data information management in the sports domain.
  • This research contributes to more efficient and accurate sports data analysis.