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一种基于殖民地优化的运动员体育行为特征的集群挖矿方法
Dapeng Yang1, Junqi Wang2, Jingtang He1
1College of Physical Education, Huainan Normal University, Huainan, 232038, China.
Heliyon
|July 18, 2024
概括
一个殖民地优化 (ACO) 集群模型改进了运动员行为分析. 这种创新方法通过准确识别运动员的特征来增强体育训练和竞争策略.
科学领域:
- 运动科学 运动科学 运动科学
- 数据挖掘 数据挖掘
- 计算智能是一种计算智能.
背景情况:
- 分析运动员的行为对于优化体育表现至关重要.
- 体育领域的高维数据带来了重大集群挑战.
- 现有的集群方法可能无法完全捕捉复杂的行为模式.
研究的目的:
- 介绍和评估一个殖民地优化 (ACO) 集群模型用于运动员行为分析.
- 解决体育数据中的高维分类问题.
- 提高运动员行为特征分析的精度,以优化训练和战略.
主要方法:
- 开发了一种创新的殖民地优化 (ACO) 集群模型.
- 模拟的食行为,用于集群中的路径选择.
- 精心调整的ACO参数,并针对特定运动的功能进行优化.
- 与神经网络,支持矢量机器和深度学习模型相比,ACO模型的性能比较.
主要成果:
- ACO模型表现出优异的性能,轮系数为0.72,戴维斯-博尔丁指数为1.05.
- 达到0.82的高回忆率,表明准确捕捉运动员的行为特征.
- 在有效性和稳定性方面表现优于传统和先进的集群算法.
- 验证了ACO模型在分析复杂,高维度运动员行为数据中的可靠性.
结论:
- 优化的ACO集群模型为了解运动员行为提供了一种新且有效的方法.
- 这种方法显著推进了体育科学研究和实际应用.
- 对于更大的数据集和不同的体育数据类型,需要进一步验证.
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