An Advanced Apriori Algorithm Technology to Enhance Sports Data Mining

Abstract

This paper explores the optimization of association rule mining in the context of physical education (PE) data, focusing on modifying the Apriori algorithm to enhance its efficiency and accuracy. Recognizing the unique characteristics of PE data—such as the consistent length and limited scope of physical test metrics—the study proposes a novel approach that combines transaction compression with hash technology to refine the Apriori algorithm. This enhanced algorithm aims to analyze the correlations between physical fitness indicators effectively and quickly to identify key factors influencing student performance. Experimental results demonstrate that the improved model significantly increases operational efficiency while maintaining high mining accuracy. Additionally, the research addresses the integration of ecological education within PE, emphasizing the dynamic balance concept of “balance-unbalance-adaptation-balance” in optimizing the educational ecosystem. The findings are helpful for providing practical tools for educators to advance this field.