Outlier data mining model for sports data analysis
Zhimeng Yin, Wei Cui
Abstract
The results of data mining can be used to predict the physical health status of sports athletes and college sports students and provide physical fitness warnings, so that students can pay attention to physical health status and adjust their physical exercise status. Discrete Morse theory, as a powerful optimization theory, plays a big role in algorithm optimization. This paper combines data mining and discrete Morse theory to propose a grid clustering algorithm based on discrete Morse theory. Moreover, according to the theorem that the cell complex reaches the optimum when it has the smallest possible critical point, this study applies the concept of critical points in the discrete Morse theory to optimize the grid clustering process to obtain clustering results. In addition, this study uses the improved C4.5 algorithm to analyze the physical fitness assessment results and obtains a valuable analysis of the physical fitness assessment results.