A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization.

ABSTRACT: This study aims to enhance the precision of analyzing athlete behavior characteristics, thereby optimizing sports training and competitive strategies. This study introduces an innovative Ant Colony Optimization (ACO) clustering model designed to address the high-dimensional clustering issues in athlete behavior data by simulating the path selection mechanism of ants searching for food. The development process of this model includes fine-tuning ACO parameters, optimizing for features specific to sports data, and comparing it with traditional clustering algorithms, and similar research models based on the neural network, support vector machines, and deep learning. The results indicate that the ACO model significantly outperforms the comparison algorithms in terms of silhouette coefficient (0

SUBMITTER: Yang D 

PROVIDER: S-EPMC11252961 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature